Saturday, December 06, 2025

THE ENGINEER'S GUIDE TO CREATING STUNNING PRESENTATIONS

 



INTRODUCTION: WHY PRESENTATIONS MATTER IN ENGINEERING

Every engineer has sat through presentations that made them want to check their email, debug code, or contemplate the heat dissipation properties of the projector. We have also experienced those rare, brilliant presentations that made complex concepts crystal clear and left us energized and inspired. The difference between these two extremes is not luck or natural talent. It is the result of deliberate choices about design, content, and delivery that anyone can learn and apply.

As engineers, we excel at solving complex problems, optimizing systems, and building elegant solutions. Yet when it comes to presenting our work, many of us fall into common traps. We cram slides with dense bullet points copied from documentation. We use default templates that scream "I spent five minutes on this." We assume our brilliant technical work will speak for itself, forgetting that even the most sophisticated algorithm means nothing if our audience cannot understand or remember it.

This tutorial will transform how you approach presentations. You will learn the fundamental principles that separate mediocre slides from stunning ones. You will discover practical techniques for visual design, content organization, and audience engagement. Most importantly, you will gain a systematic framework for creating presentations that do justice to your technical expertise while respecting your audience's time and attention.

UNDERSTANDING YOUR AUDIENCE: THE FOUNDATION OF EVERY GREAT PRESENTATION

Before you open your presentation software or sketch a single slide, you must deeply understand who will be sitting in that room or joining that video call. This is not a trivial preliminary step. It is the foundation upon which every other decision rests. An engineer presenting a new microarchitecture to fellow chip designers needs a completely different approach than one explaining the same technology to business stakeholders or potential customers.

Consider the technical depth your audience expects and can handle. When presenting to fellow engineers in your specific domain, you can use specialized terminology, reference standard algorithms without explanation, and dive into implementation details. Your audience expects this depth and will be disappointed by oversimplification. However, when presenting to engineers from different domains, you must calibrate carefully. A software engineer may understand algorithmic complexity but not the intricacies of signal integrity. A hardware engineer may grasp circuit-level optimizations but need context about software frameworks.

The expectations extend beyond technical depth to presentation style and content focus. Engineering managers often care more about project timelines, resource allocation, and business impact than implementation details. They need the "what" and "why" more than the "how." Conversely, technical reviewers and peer engineers want to see your methodology, understand your design tradeoffs, and evaluate your technical decisions. They will scrutinize your approach and expect rigorous justification.

Time constraints also vary dramatically by audience and context. A conference presentation might give you twenty minutes to convey years of work. A design review might allow an hour for deep technical discussion. A quick status update might demand you compress everything into five minutes. These constraints fundamentally shape how many slides you prepare and how much information each slide contains.

GOLDEN RULE ONE: ONE CORE IDEA PER SLIDE

The single most common mistake in technical presentations is trying to communicate too much on a single slide. Engineers love comprehensiveness. We want to show all the data, explain every detail, and leave no question unanswered. This instinct, while admirable in documentation, destroys presentations. When a slide tries to communicate five different concepts, your audience absorbs none of them effectively.

Each slide should convey exactly one core idea. This does not mean each slide contains only one sentence or one graphic. It means that when someone looks at that slide, they should immediately grasp what single point you are making. Everything on that slide should support and reinforce that one idea. If you find yourself thinking "this slide covers our architecture and our performance results and our power consumption," you actually have three slides, not one.

Let me illustrate with a concrete example. Imagine you are presenting a new caching algorithm you developed. A bad slide might have the title "New Caching Algorithm" and then contain bullet points describing the algorithm, a code snippet showing the implementation, a graph of performance improvements, a table comparing it to existing approaches, and a list of use cases. Your audience will not know where to look first. They will struggle to understand the relationships between these elements. They will miss your key message while trying to process everything simultaneously.

Instead, break this into multiple focused slides. One slide introduces the problem your algorithm solves, perhaps showing a simple diagram of cache misses in the existing system. Another slide presents the core insight of your algorithm, maybe with a visual representation of how it makes decisions differently. A third slide shows the implementation approach with carefully selected code. A fourth slide presents performance data. A fifth compares your approach to alternatives. Each slide has one clear purpose, and your narrative flows naturally from one to the next.

This approach requires more slides, but those slides are more effective. Your audience can absorb each point fully before moving to the next. You can build understanding progressively rather than overwhelming people with information. You also gain better control over pacing and emphasis during your presentation.

GOLDEN RULE TWO: VISUAL HIERARCHY GUIDES THE EYE

When someone looks at your slide, their eye does not process all elements equally. Humans naturally focus on larger elements before smaller ones, high-contrast elements before low-contrast ones, and elements positioned in certain areas before others. This is not a bug in human perception. It is a feature you can exploit to guide your audience's attention exactly where you want it.

Visual hierarchy means deliberately designing your slides so the most important information draws attention first, secondary information comes next, and supporting details are available but do not compete for focus. Size is your most powerful tool for creating hierarchy. Your slide title should be the largest text element, immediately communicating what this slide is about. Your main content should be the next largest. Supporting labels, citations, and footnotes should be smaller.

Color and contrast create hierarchy as well. Elements with strong contrast against the background pop forward. Elements with subtle contrast recede into the background. If you have a key number or finding you want to emphasize, make it larger and use a bold, saturated color. If you have supporting context that should be available but not distracting, use a muted color with less contrast.

Position matters tremendously. In Western cultures, people naturally start reading from the top left and move right and down. Place your most important content where eyes naturally go first. If you have a key graph, do not bury it in the bottom right corner. Put it front and center where it commands attention.

Consider a slide showing performance benchmarks for your new optimization. A poor design might have a title in small font, multiple graphs of equal size scattered across the slide, and dense axis labels competing for attention. Your audience will not know which graph matters most or what conclusion they should draw. A strong design would have a clear, large title stating your key finding, such as "New Optimization Achieves 3X Speedup on Real Workloads." The most important graph showing this result would be large and centrally positioned. Supporting graphs showing performance under different conditions would be smaller. The critical 3X number might be highlighted in a bold color. This hierarchy tells your audience exactly what matters and what they should remember.

GOLDEN RULE THREE: EMBRACE WHITE SPACE

Engineers often treat slide real estate like memory in an embedded system with severe constraints. Every pixel must be utilized. Every blank area represents wasted opportunity to convey more information. This mindset produces cluttered, overwhelming slides that exhaust your audience.

White space, also called negative space, is not wasted space. It is active design element that makes your content more comprehensible and visually appealing. White space gives your audience's eyes places to rest. It creates clear separation between different elements so relationships are obvious. It makes your slides look professional and confident rather than desperate and cluttered.

Think about the difference between reading a dense academic paper with tiny margins and narrow line spacing versus reading a well-designed book with generous margins and breathing room. The content might be identical, but the experience is completely different. The same principle applies to slides, except more so because your audience must process slides in real time while listening to you speak.

Aim to use only about half of your slide area for content. This might feel wasteful at first, but the results speak for themselves. A slide with a single powerful graph surrounded by white space is far more effective than a slide crammed with four graphs competing for attention. A slide with three carefully worded sentences and ample spacing is more readable than a slide with ten bullet points in small font.

White space also helps with the one-idea-per-slide principle. If you are struggling to fit everything on a slide while maintaining adequate white space, that is a signal you are trying to communicate too much. Split the content across multiple slides, each with room to breathe.

GOLDEN RULE FOUR: CHOOSE FONTS DELIBERATELY

Typography might seem like a superficial concern compared to your technical content, but font choices dramatically impact readability and audience perception. Poor font choices make your slides harder to read, look amateurish, and distract from your message. Good font choices enhance clarity and convey professionalism.

For presentation slides, you need fonts that remain readable when projected on a screen or viewed on various devices. This means sans-serif fonts are almost always the right choice. Fonts like Arial, Helvetica, Calibri, and Open Sans work well because their clean, simple letterforms remain clear even at a distance or on low-resolution displays. Serif fonts like Times New Roman, while perfectly fine for printed documents, have small decorative strokes that can blur together when projected.

Font size matters even more than font choice. Your smallest text should be no smaller than 24 points, and that should be reserved only for citations or minor labels. Your main content should be 28 to 32 points minimum. Your titles should be 36 to 44 points. These sizes might feel enormous when you are creating slides on your laptop, but remember that your audience will be viewing them from much farther away or on smaller screens during video calls.

Limit yourself to two fonts maximum in a presentation, and one font is often better. You might use one font for titles and another for body text, but introducing a third font for special emphasis or a fourth for code examples creates visual chaos. Instead, use variations of the same font family. Most professional fonts come in multiple weights like light, regular, medium, and bold. Use these weights to create hierarchy and emphasis while maintaining visual consistency.

For code examples, you must use a monospace font where every character takes the same width. This preserves the alignment and indentation that makes code readable. Good choices include Consolas, Monaco, Source Code Pro, and Fira Code. Make sure your code font size is large enough to read comfortably. Code on slides should be 20 to 24 points minimum, even though this means you can show fewer lines. If your code example does not fit at readable size, you are showing too much code on one slide.

GOLDEN RULE FIVE: COLOR WITH PURPOSE, NOT DECORATION

Color is one of the most powerful tools in your design arsenal, but also one of the most misused. Many presentations use color arbitrarily, with different elements in different colors for no particular reason except that the default template provided those colors. This creates visual noise without adding meaning. Other presentations avoid color almost entirely, producing monotonous gray slides that fail to engage.

Effective color use is purposeful. Every color choice should serve a specific function. Color can encode information, create emphasis, establish visual relationships, or guide attention. When color serves one of these purposes, it enhances your presentation. When color is arbitrary or decorative, it distracts.

Use color to encode information when you have categories or groups to distinguish. If you are comparing three different algorithms, assign each a distinct color and use that color consistently throughout your presentation. When your audience sees that blue line in a graph, they immediately know it represents Algorithm A without having to check the legend. This consistency reduces cognitive load and makes your data easier to understand.

Use color for emphasis when you want to draw attention to specific elements. If you have a table of benchmark results and want to highlight the best performance, make that cell a bold color while keeping others neutral. If you have a diagram with many components but want to focus on one particular module, make that module a saturated color and render the others in muted tones.

Be extremely careful with color combinations. Some combinations are hard to read, especially for people with color vision deficiencies. Red text on green background or vice versa is particularly problematic. Approximately eight percent of men and half a percent of women have some form of color blindness, most commonly red-green color blindness. Design your slides so information is conveyed through more than just color. Use both color and shape, or color and labels, or color and position.

Maintain adequate contrast between text and background. Light text on dark background or dark text on light background both work well. Medium-contrast combinations like gray text on white background can work for de-emphasized content but should never be used for important information. Test your slides by viewing them on different displays and in different lighting conditions. What looks fine on your laptop might be unreadable when projected.

GOLDEN RULE SIX: TEMPLATES ESTABLISH CONSISTENCY

A template is more than just a decorative border or background pattern. It is a systematic framework that establishes consistent positioning, sizing, and styling across all your slides. This consistency helps your audience focus on content rather than constantly adjusting to different layouts.

Many engineers use whatever default template their presentation software provides, which often means the generic corporate template with a logo in the corner and perhaps a colored bar across the top. While these templates are better than no template at all, they rarely reflect the care and professionalism your technical work deserves. Creating or customizing a template takes time upfront but pays dividends across every presentation you create.

Your template should define standard positions for common elements. Titles should always appear in the same location at the same size. If you use slide numbers or section indicators, they should always be in the same corner. If you include your name or project name on slides, it should be consistently positioned. This positional consistency means your audience knows where to look for information and is not distracted by layout variations.

The template should also define a color scheme that you use consistently. Choose a primary color for emphasis and key elements, a secondary color for supporting information, and neutral colors for backgrounds and de-emphasized content. Having these colors predefined in your template ensures you use them consistently rather than picking slightly different shades each time.

For technical presentations, your template should include layouts optimized for common slide types. You will need a title slide layout, a section divider layout, a layout for slides with a single large graphic, a layout for slides with text and a supporting image, and a layout for code examples. Having these predefined layouts means you spend less time fiddling with positioning and more time focusing on content.

Simplicity in template design is crucial. Elaborate backgrounds, decorative graphics, and complex borders compete with your content for attention. The best templates are almost invisible, providing structure and consistency without drawing attention to themselves. A simple solid color background or subtle gradient is almost always better than a busy pattern or photographic background.

GOLDEN RULE SEVEN: FIGURES AND DIAGRAMS CLARIFY COMPLEXITY

Engineers work with complex systems, algorithms, and architectures that are difficult to convey through text alone. Well-designed figures and diagrams can communicate in seconds what would take paragraphs to explain. However, poorly designed visuals can confuse rather than clarify.

Every figure or diagram you include must earn its place on the slide. Ask yourself what specific understanding this visual creates that words alone cannot. If you cannot articulate the purpose clearly, the figure probably does not belong. Generic stock photos or decorative graphics that do not convey specific information waste valuable slide space and cognitive bandwidth.

When creating technical diagrams, clarity trumps completeness. You might have a system architecture with dozens of components and hundreds of connections, but showing all that detail on a slide creates an incomprehensible tangle. Instead, show the level of detail appropriate for the point you are making. If you are explaining the high-level data flow, show only the major components and primary paths. If you need to dive into a specific subsystem later, create a separate diagram zooming into that area.

Labels and annotations on figures must be large enough to read easily. A diagram with tiny, unreadable labels is worse than no diagram at all because it frustrates your audience. If you are importing a figure from a paper or tool, you will almost certainly need to recreate or modify it with larger text. Axis labels on graphs should be at least 18 to 20 points. Component labels in diagrams should be similarly sized.

Use visual encoding effectively in diagrams. Different shapes can represent different types of components. Different colors can show different subsystems or data types. Arrows can indicate flow or dependencies. Line thickness can represent bandwidth or importance. However, always include a legend or labels so your encoding is explicit. Never assume your audience will intuit what your visual conventions mean.

Consider this example of showing a caching system architecture. A poor diagram might show every cache level, every bus connection, every control signal, and every data path in a dense, tangled mess. Labels might be tiny and overlap. The audience would struggle to understand even the basic structure. A strong diagram would show the key components at an appropriate level of abstraction. The CPU, L1 cache, L2 cache, L3 cache, and main memory might be represented as clean boxes with clear labels. Arrows would show the primary data paths. A simple color scheme might distinguish different memory hierarchy levels. This simplified diagram conveys the essential architecture without overwhelming detail.

GOLDEN RULE EIGHT: GRAPHS AND CHARTS MUST TELL A STORY

Engineers love data, and presentations are full of graphs showing performance metrics, benchmark results, statistical analyses, and experimental measurements. However, a graph is not inherently informative. A poorly designed graph can obscure insights rather than reveal them. Your graphs must be designed to tell a specific story and guide your audience to the right conclusions.

Start by choosing the right graph type for your data and message. Line graphs show trends over time or across a continuous variable. Bar charts compare discrete categories. Scatter plots reveal correlations or distributions. Box plots show statistical distributions. Choosing the wrong graph type makes your data harder to understand. If you want to compare the performance of five different algorithms, a bar chart makes the comparison immediate and obvious. A line graph would be confusing because there is no continuous relationship between algorithms.

Every graph needs clear, large axis labels that explain what is being measured and in what units. "Latency (milliseconds)" is informative. "Latency" is less clear. "Time" is ambiguous. Unlabeled axes are unacceptable. Your audience should never have to guess what your axes represent.

The title or caption of your graph should state the key finding, not just describe the content. "Performance Comparison" is a weak title that simply labels what the graph shows. "New Algorithm Achieves 40% Lower Latency" is a strong title that tells your audience what conclusion they should draw. This approach, sometimes called a "takeaway title," makes your graphs self-explanatory and reinforces your key messages.

Simplify your graphs ruthlessly. Remove gridlines unless they are essential for reading values. Eliminate unnecessary decimal precision in axis labels. Remove or minimize legends by labeling data series directly on the graph when possible. Reduce the number of data series shown to only those relevant for the current point. If you have benchmark data for twenty different configurations but only three are relevant to your current argument, show only those three.

Use color and visual weight to emphasize the most important data. If you are comparing your new approach to several baselines, make your approach a bold, saturated color and render the baselines in muted tones. This immediately directs attention to what matters most.

Imagine you are showing benchmark results comparing your optimized code to the baseline. A weak graph might show ten different metrics across five different workloads in a dense, multi-colored tangle of lines with a tiny legend in the corner. The axes might be labeled simply "Performance" and "Workload" without units or clear scale. Your audience would struggle to extract any meaning. A strong graph would focus on the single most important metric, perhaps showing execution time for your optimized version versus baseline across the five workloads as a clean bar chart. The bars for your optimized version might be blue, the baseline bars gray. The y-axis would be clearly labeled "Execution Time (seconds)" with an appropriate scale. The title would state "Optimization Reduces Execution Time by 35% on Average." This graph tells a clear story and supports your narrative.

GOLDEN RULE NINE: TABLES ORGANIZE DETAILED COMPARISONS

While graphs excel at showing trends and patterns, tables are the right choice when you need to present precise values for comparison or when you have multiple attributes to show for each item. However, tables on slides require even more care than tables in documents because your audience has limited time to process the information.

Keep tables small and focused. A table with twenty rows and ten columns might be appropriate in a technical report where readers can study it carefully, but on a slide it becomes an unreadable wall of numbers. Limit tables to the most important rows and columns. If you have extensive data, show a subset that illustrates your point and mention that complete data is available in your documentation.

Format tables for readability. Use adequate spacing between rows and columns so values do not run together. Alternate row shading can help the eye track across rows in larger tables. Align numbers consistently, typically right-aligned or decimal-aligned so magnitudes are easy to compare. Text is usually left-aligned.

Just as with graphs, use color or bold formatting to emphasize the most important values. If you have a comparison table showing your approach versus alternatives across multiple metrics, highlight the best value in each row. This guides your audience to the key findings without requiring them to scan and compare all values mentally.

Column and row headers must be clear and include units where appropriate. "Power" is ambiguous. "Power (Watts)" is clear. "Latency" could mean many things. "Average Latency (ms)" is specific.

Consider a table comparing three different processor architectures across several metrics. A poor table might have tiny text, no spacing between rows, unclear abbreviations in headers, and no visual emphasis. Your audience would struggle to read the values and would not know which comparisons matter most. A strong table would have clear headers like "Architecture," "Clock Speed (GHz)," "Power (Watts)," and "Performance (SPECint)." The text would be large enough to read easily. Row spacing would provide visual separation. The best value in each metric column might be highlighted in bold or color. This table would support quick comprehension and comparison.

GOLDEN RULE TEN: CODE EXAMPLES MUST BE MINIMAL AND ANNOTATED

For software engineers, showing code examples is often essential to explain implementation approaches, demonstrate APIs, or illustrate algorithms. However, code on slides presents unique challenges. Code is dense, detail-oriented, and requires careful reading. Your audience cannot pause and study code the way they might when reading documentation.

The cardinal rule for code on slides is to show the absolute minimum necessary to make your point. If you are explaining a new API, show a simple, clear example of its use, not a complete production-ready implementation with error handling, edge cases, and optimizations. If you are illustrating an algorithm, show the core logic, not the entire function with initialization, cleanup, and helper routines.

Simplify code examples even beyond what would be acceptable in real implementation. Remove error handling if it is not relevant to your point. Use simplified variable names if that improves clarity. Add comments that would be redundant in real code but help your audience follow along. Your goal is not to show production-ready code but to communicate a concept.

Format code with generous spacing and large font size. Code should be at least 20 to 24 points, which means you can show perhaps ten to fifteen lines maximum on a slide. If your example does not fit in that space, it is too complex for a slide. Break it into multiple slides showing different parts, or simplify further.

Use syntax highlighting to make code structure clear. Keywords, strings, comments, and identifiers should be visually distinct. Most presentation software can import code with syntax highlighting from your development environment, or you can use online tools to generate highlighted code snippets.

Annotate code examples to guide your audience's attention. You might use arrows or callout boxes to highlight specific lines or sections. You might use color overlays to emphasize the key parts while de-emphasizing boilerplate. You might build up code progressively across multiple slides, showing a skeleton first and then adding details.

Consider an example where you want to show how to use a new caching API you developed. A poor slide might show a complete function with variable declarations, error checking, cache initialization, data processing, cache updates, cleanup, and return statements crammed into tiny font. Your audience would be overwhelmed and unable to focus on the API usage pattern. A strong slide would show just the essential API calls with clear comments. Perhaps something like this conceptual example:

// Initialize cache with capacity
cache = create_cache(1000);

// Check if data is cached
if (cache_contains(cache, key)) {
    data = cache_get(cache, key);
} else {
    data = compute_expensive_result(key);
    cache_put(cache, key, data);
}

This simplified example shows the key API functions and usage pattern without extraneous details. The font would be large and readable. Syntax highlighting would make the structure clear. You might use arrows or highlights to emphasize the three main API functions being demonstrated.

GOLDEN RULE ELEVEN: ANIMATIONS SERVE PURPOSE, NOT FLASH

Animations and transitions are among the most controversial elements in presentation design. Some presenters avoid them entirely, viewing them as unprofessional gimmicks. Others use them liberally, with every bullet point flying in from a different direction and slides transitioning with elaborate effects. The truth lies between these extremes.

Animations can serve legitimate pedagogical purposes when used thoughtfully. The key question is whether an animation helps your audience understand your content better than a static slide would. If the answer is yes, use the animation. If the answer is no, skip it.

Progressive disclosure is one of the most valuable animation techniques for technical presentations. When you have a complex diagram or multi-step process, showing everything at once can overwhelm your audience. Instead, build the diagram piece by piece, adding each component as you explain it. This guides attention and allows you to construct understanding progressively.

For example, if you are explaining a multi-stage processing pipeline, you might start with just the input and first stage visible. As you explain that stage, it appears. Then you reveal the second stage and explain it. This continues until the complete pipeline is visible. Your audience builds understanding incrementally rather than trying to process the entire system at once.

Emphasis animations can highlight specific elements while you discuss them. If you have a diagram with multiple components and want to focus on one particular component, you might dim the others or highlight the relevant one. This directs attention and makes clear what you are discussing.

Transitions between slides should be simple and consistent. A subtle fade or quick cut is professional and unobtrusive. Elaborate transitions like spinning cubes, checkerboards, or dissolves distract from content and look amateurish. Choose one simple transition and use it throughout your presentation.

Avoid animations that serve no purpose except decoration. Bullet points that fly in from the side, text that spirals into place, or graphics that bounce do not enhance understanding. They annoy your audience and make your presentation feel less professional.

Timing is crucial for any animation. Animations should be quick enough to feel responsive but not so fast that they are jarring. Generally, animations should complete in half a second to one second. Slower animations make your presentation feel sluggish and waste your audience's time.

Always have a backup plan for environments where animations might not work. If you are presenting on an unfamiliar system or sharing slides as a PDF, animations will be lost. Your slides should still be comprehensible and effective in static form.

GOLDEN RULE TWELVE: SLIDE COUNT MATCHES PRESENTATION TIME

One of the most common questions presenters ask is how many slides they should prepare. Unfortunately, there is no universal answer because the appropriate number depends on your content, your speaking style, and your time constraints. However, you can use some general guidelines to estimate.

For technical presentations with substantial content on each slide, a reasonable pace is roughly one to two minutes per slide. This gives you time to introduce the slide, explain the content, and transition to the next point. At this pace, a twenty-minute presentation might have ten to twenty slides. A forty-five-minute presentation might have twenty-five to forty slides.

However, this guideline varies significantly based on slide complexity. A slide with a simple title and one key statement might take only thirty seconds to present. A slide with a complex diagram or detailed graph might require three to four minutes of explanation. Build your presentation and then practice it with a timer to see if your pacing works.

Avoid the trap of preparing too many slides and then rushing through them. If you find yourself saying "I will skip this slide in the interest of time" or clicking rapidly through slides without adequate explanation, you have too many slides. It is far better to cover less material thoroughly than to race through more material superficially.

Different types of slides require different amounts of time. Title slides and section dividers might be visible for only ten to fifteen seconds as you transition between topics. Slides with key findings or important graphs might deserve two to three minutes. Budget your time accordingly when planning your presentation.

For different presentation contexts, adjust your slide count strategy. A conference talk with a hard time limit requires careful planning to ensure you finish on time. Build in a small buffer so you do not have to rush at the end. A design review or technical discussion might be more flexible, allowing you to spend extra time on slides that generate questions or interest. A recorded presentation that people will watch asynchronously might benefit from more slides with less content each, since viewers can pause and replay as needed.

GOLDEN RULE THIRTEEN: OPENING AND CLOSING SLIDES FRAME YOUR MESSAGE

The first and last slides of your presentation carry disproportionate weight. The opening slide sets expectations and captures attention. The closing slide leaves a lasting impression and reinforces your key message. Both deserve special attention.

Your opening title slide should immediately communicate what your presentation is about and why it matters. A generic title like "Project Update" tells your audience almost nothing. A specific title like "Reducing Memory Latency in Distributed Caching Systems: A Novel Prefetching Approach" tells your audience exactly what you will discuss. Include your name, affiliation, and date so the slide is self-contained and useful if shared later.

Consider including a brief outline or agenda slide early in your presentation, especially for longer talks. This roadmap helps your audience understand the structure and know what to expect. However, keep the outline high-level. Listing every single topic you will cover creates a dense, intimidating slide. Instead, show the three to five major sections or themes.

Your closing slide should not simply say "Thank You" or "Questions?" While these are common practices, they waste an opportunity to reinforce your message. Instead, your final slide should restate your key takeaway or conclusion. What is the one thing you want your audience to remember? State it clearly on your final slide so it remains visible during questions and discussion.

You might also include your contact information on the final slide so people can reach you afterward. An email address or professional social media handle gives interested audience members a way to continue the conversation.

Some presentations benefit from a summary slide before the conclusion, especially if you have covered multiple complex topics. This summary should be concise, highlighting the three to five most important points. Think of it as the abstract of your presentation, capturing the essential message in a form that someone could understand even without seeing the full talk.

GOLDEN RULE FOURTEEN: CONSISTENCY CREATES PROFESSIONALISM

Throughout your presentation, consistency in design, terminology, and style signals professionalism and helps your audience focus on content rather than being distracted by variations. Inconsistency, even in small details, creates a sense of sloppiness that undermines your credibility.

Use consistent terminology throughout your presentation. If you call something a "cache controller" on one slide, do not call it a "cache manager" or "caching subsystem" on another slide unless you are specifically distinguishing between different components. This seems obvious, but in long presentations developed over time, terminology drift is common. Review your slides specifically for consistent naming.

Maintain consistent formatting for similar elements. If you show code examples on multiple slides, they should all use the same font, size, and syntax highlighting scheme. If you have multiple graphs, they should use the same color scheme and axis label formatting. If you have several diagrams, they should use the same visual style and conventions.

Be consistent in how you cite sources or reference related work. If you include citations, use the same format throughout. If you reference figures or tables by number, use a consistent numbering scheme.

Consistency extends to your speaking style and delivery, though that is beyond the scope of slide design. However, your slides should support consistent delivery by having a predictable structure that you can follow naturally.

GOLDEN RULE FIFTEEN: TEST AND ITERATE BEFORE PRESENTING

Even the most carefully designed slides benefit from testing and refinement. What seems clear when you are creating slides might be confusing to others. What looks good on your laptop might be unreadable when projected. What fits comfortably in your planned time might actually take much longer to explain.

Practice your presentation multiple times before the actual event. Time yourself to ensure you can cover all your material in the allotted time. Pay attention to which slides take longer than expected and which points generate confusion. Be willing to cut or simplify content that does not fit.

If possible, present to a colleague or friend and get feedback. Ask them what they understood, what confused them, and what they remember. Their perspective will reveal issues you cannot see because you are too close to the material.

Test your slides on the actual presentation equipment if you can. Projectors vary in brightness, color accuracy, and resolution. What looks great on your high-resolution laptop screen might look washed out or blurry when projected. If you cannot test on the actual equipment, at least view your slides on a different display and from across the room to simulate the viewing experience.

Check that all animations work as expected and that any embedded videos or interactive elements function properly. Have a backup plan for technical failures. Can you present effectively if animations do not work? Do you have a PDF version of your slides as a fallback?

Review your slides one final time specifically for errors. Check spelling, grammar, and technical accuracy. Verify that all numbers and citations are correct. A single obvious error can undermine your credibility and distract your audience.

PRACTICAL EXAMPLE: REDESIGNING A TYPICAL ENGINEERING SLIDE

To make these principles concrete, let us walk through the process of transforming a typical poor engineering slide into an effective one. Imagine you are presenting research on optimizing database query performance, and you have a slide about your experimental results.

The original slide might look something like this in concept. The title at the top says "Results" in a modest font. Below that are four bullet points in small text describing different aspects of your findings: "Tested on three database systems," "Workload consisted of TPC-H benchmark queries," "Measured query execution time and resource utilization," and "Achieved significant performance improvements." Below the bullets is a dense graph showing multiple overlapping lines in different colors with a tiny legend. To the side is a small table with numbers in a font too small to read comfortably. At the bottom is a footnote referencing your experimental setup document.

This slide violates nearly every principle we have discussed. It tries to communicate too much at once. The title is generic and uninformative. The bullet points provide context but do not state findings. The graph and table compete for attention. Text is too small. The visual hierarchy is unclear. The key finding is buried rather than emphasized.

Let us redesign this as a series of focused slides. The first slide in the sequence has a clear, informative title: "Query Optimization Reduces Execution Time by 45% on TPC-H Benchmark." This immediately tells your audience the key finding. Below the title is a single, large, clean bar chart comparing average query execution time for the baseline system versus your optimized version across the three database systems you tested. The bars for your optimized version are in a bold blue color. The baseline bars are in a neutral gray. The y-axis is clearly labeled "Average Query Time (seconds)" with an appropriate scale. The x-axis shows the three database systems with clear labels. The graph is large enough to read easily and is surrounded by adequate white space. There are no distracting gridlines or unnecessary decorative elements.

The second slide in the sequence might dive deeper into the results for one particular database system, showing a more detailed breakdown of performance across different query types. The title might be "Optimization Benefits Vary by Query Complexity." The graph would show performance improvement as a percentage for different categories of queries. Again, the graph is large, clear, and focused on a single point.

A third slide might show resource utilization data, with a title like "Performance Gains Achieved Without Increasing Memory Usage." This slide would have a different graph showing memory consumption for baseline versus optimized versions, demonstrating that your optimization does not trade performance for memory.

By breaking the original cluttered slide into three focused slides, each with a clear message and supporting visual, you create a much more effective presentation. Your audience can absorb each finding fully. The visual hierarchy guides their attention. The titles tell them what conclusions to draw. The graphs are readable and informative.

UNDERSTANDING DIFFERENT PRESENTATION CONTEXTS

The principles we have discussed apply broadly, but different presentation contexts require different emphases and adaptations. A conference presentation, an internal design review, a customer demonstration, and a recorded tutorial each have distinct characteristics that shape your approach.

Conference presentations typically have strict time limits and large, diverse audiences. You cannot assume deep familiarity with your specific problem domain. Your slides need to provide more context and background than they would for a specialist audience. You also need to be more selective about technical depth, focusing on key insights and high-level approaches rather than implementation details. Visual appeal matters more in conference settings because you are competing for attention and trying to make a memorable impression.

Design reviews and technical meetings usually involve smaller, more specialized audiences who expect and can handle significant technical depth. Your slides can use domain-specific terminology without extensive explanation. You might include more detailed diagrams, more comprehensive data, and more thorough methodology descriptions. However, you should still follow the core principles of clarity, focus, and visual hierarchy. Even expert audiences benefit from well-designed slides.

Customer or stakeholder presentations require translating technical work into business value and practical benefits. Your slides should emphasize outcomes, capabilities, and advantages rather than implementation details. You might include more application examples and use cases. Visual polish is important because you are representing your organization and trying to build confidence.

Recorded presentations that people will watch asynchronously allow for different pacing and structure. Viewers can pause, rewind, and replay sections, which means you can include more dense content than you would in a live presentation. However, you also lose the ability to gauge audience reaction and adjust your pacing. Clear structure and navigation become more important. You might include more explicit signposting and transitions to help viewers follow along.

ADDRESSING COMMON MISTAKES AND HOW TO AVOID THEM

Even experienced presenters fall into common traps that undermine their presentations. Being aware of these pitfalls helps you avoid them.

Reading directly from slides is perhaps the most common and damaging mistake. If your slides contain complete sentences that you simply read aloud, your audience will wonder why they needed to attend. They can read faster than you can speak. Your slides should support and complement your spoken words, not duplicate them. Use brief phrases, key terms, and visuals on slides while you provide the explanation and context verbally.

Using too much text is closely related. Slides are not documents. If you find yourself putting paragraphs of text on slides, you are creating a document, not a presentation. Convert that text into a handout or supplementary document and redesign your slides to be visual and concise.

Inconsistent or missing labels on graphs and diagrams frustrate your audience and force them to guess at meanings. Every axis, every data series, every component in a diagram should be clearly labeled. Units should always be specified. Legends should be readable.

Overcomplicating visuals with unnecessary 3D effects, shadows, gradients, and decorative elements distracts from content. Modern design trends favor flat, clean aesthetics for good reason. They are easier to read and look more professional. Resist the temptation to use every visual effect your software offers.

Ignoring color accessibility can exclude audience members with color vision deficiencies. Always ensure that information is conveyed through more than just color. Use both color and shape, or color and labels, or color and pattern. Test your slides in grayscale to verify that they remain comprehensible.

Underestimating the time required for each slide leads to rushed presentations where you skip content or race through explanations. Always practice with a timer and build in buffer time for questions and unexpected delays.

RESOURCES AND TOOLS FOR CREATING BETTER SLIDES

While the principles we have discussed are more important than any specific tool, having the right resources can make implementation easier. Modern presentation software offers powerful capabilities, but you need to know how to use them effectively.

Most engineers use PowerPoint, Keynote, or Google Slides for presentations. All three are capable tools that can produce excellent results when used well. PowerPoint is ubiquitous in corporate environments and offers extensive features. Keynote is popular among Mac users and is known for producing visually polished results with less effort. Google Slides offers collaboration features and cloud-based access.

For engineers comfortable with code, there are also text-based presentation tools like LaTeX Beamer or reveal.js that allow you to create slides using markup languages. These tools offer precise control and integrate well with version control systems, but have steeper learning curves.

Regardless of which tool you use, invest time in learning its advanced features. Master slide templates, custom color schemes, alignment guides, and animation controls. Knowing your tools well allows you to implement your designs efficiently.

For creating diagrams and technical illustrations, specialized tools often produce better results than the drawing tools built into presentation software. Tools like draw.io, Lucidchart, or OmniGraffle are designed for creating technical diagrams and offer features like smart connectors, alignment, and component libraries. You can create diagrams in these tools and import them into your slides.

For graphs and charts, consider creating them in specialized data visualization tools or programming libraries and importing them as high-quality images. Tools like Python's matplotlib, R's ggplot2, or JavaScript's D3.js offer fine-grained control over every aspect of your visualizations. While presentation software can create basic charts, specialized tools give you more control over design and can handle more complex data.

Color scheme generators like Adobe Color or Coolors can help you choose harmonious color combinations. These tools let you explore different color palettes and ensure adequate contrast.

Icon libraries like Font Awesome or the Noun Project provide professional-quality icons you can use to enhance diagrams and slides. Icons can convey concepts quickly and add visual interest without clutter.

CONCLUSION: FROM PRINCIPLES TO PRACTICE

Creating stunning presentations is not about artistic talent or expensive software. It is about applying systematic principles that enhance clarity, focus attention, and respect your audience's time and cognitive capacity. Every guideline in this tutorial serves the fundamental goal of communicating your technical work effectively.

Start with a deep understanding of your audience and what they need from your presentation. Build your content around one clear idea per slide. Use visual hierarchy, white space, and color purposefully to guide attention. Choose readable fonts and maintain consistency. Create figures, graphs, and diagrams that clarify rather than confuse. Show minimal, annotated code examples. Use animations only when they serve a pedagogical purpose. Match your slide count to your available time. Frame your message with strong opening and closing slides. Test and iterate before presenting.

These principles require practice to internalize and apply naturally. Your first attempts at redesigning slides according to these guidelines will take time and effort. You will need to resist the temptation to cram more content onto each slide. You will need to simplify diagrams that you worked hard to create in full detail. You will need to cut content that does not fit in your time budget.

However, the results are worth the effort. Well-designed presentations make your technical work more accessible, more memorable, and more impactful. They demonstrate professionalism and respect for your audience. They help you communicate complex ideas clearly and persuasively. They transform presentations from a necessary chore into an opportunity to share your expertise and insights effectively.

The next time you need to create a presentation, refer back to these principles. Plan your content deliberately. Design your slides thoughtfully. Practice your delivery carefully. Your audience will notice the difference, and your technical work will receive the clear, compelling presentation it deserves.

Catch Me If You Can: Reinforcement Learning in a Minimal Arcade World



Introduction


Reinforcement Learning (RL) has become one of the most talked-about subfields of Artificial Intelligence, not only in academia but increasingly in engineering practice. Whenever autonomous systems adapt in uncertain environments — whether a robot learns to walk, a fleet of taxis self-organizes, or a data center balances workloads — RL concepts play a role. Yet many software engineers view RL as an impenetrable forest of mathematics, neural networks, and jargon.


This article proposes a different approach: we shrink the world to the size of a tiny arcade game. In this minimalist world, the essential RL principles appear clearly, free of the distractions of industrial complexity. By the end, you will not only understand the famous Q-learning algorithm but also appreciate why reinforcement learning matters for software engineers building the next generation of adaptive systems.


The Arcade World: A Minimal Environment


The game is called Catch. Imagine a grid of width W and height H. A ball appears at the top row and falls straight down, one step at a time. At the bottom, a paddle moves horizontally: left, right, or stay. If the paddle aligns with the ball when it hits the ground, the player scores; otherwise, they fail.


+-------------------+   y = H -1

|                   |

|         (ball)    |

|                   |

|                   |

|                   |

|                   |

|      [paddle]     |   y = 0

+-------------------+


The rules are minimal, the dynamics predictable. And yet, this world suffices to illustrate everything we need: states, actions, rewards, exploration, exploitation, and policy learning.



The Feedback Loop: Agent and Environment


Reinforcement learning is not about training with labeled data or finding clusters. It is about interaction. An agent repeatedly acts in an environment and is judged by the consequences. The cycle looks like this:


      ┌─────────────┐

      │             

      │   Agent     

      │             

      └─────┬───────┘

            │ action a_t

            

      ┌─────────────┐

      │             

      │ Environment │

      │             

      └─────┬───────┘

   reward r │ state s_{t+1}

            


In Catch:

State (s): the ball’s x,y position and the paddle’s x position.

Action (a): move left, stay, move right.

Reward (r): +1 if caught, −1 if missed, 0 otherwise.


This loop repeats until the ball reaches the bottom. Then the episode ends, and the next begins.



Formalizing the Challenge: The MDP


Professionals like rigor. RL is formally described as a Markov Decision Process (MDP). An MDP is defined by:

A set of states S.

A set of actions A.

A transition function P(s’|s,a).

A reward function R(s,a).

A discount factor Î³  [0,1].


The Markov property states that the next state depends only on the current state and action, not on the full history. In Catch, this property holds: the ball’s and paddle’s current positions fully determine the future.


The Dilemma: Exploration vs. Exploitation


Engineers face trade-offs daily: should we refactor for long-term health or ship features now? RL encapsulates a similar tension: explore new actions or exploit known good actions?


ASCII sketch of the dilemma:


Exploration: try something new ──► possible higher future reward

Exploitation: stick to best known ──► reliable immediate reward


The standard technique is Îµ-greedy:


if random() < ε:

    choose random action   # exploration

else:

    choose argmax_a Q(s,a) # exploitation



At the start of training, ε is high (lots of exploration). Over episodes, ε decays, allowing convergence to exploitation. The decay is usually exponential or linear, as illustrated below:


ε

^

|\

| \

 \

|   \                      Exploration ↓

|    \__________________   Exploitation ↑

+-----------------------> Episodes


This curve reminds us that exploration is front-loaded: in early episodes, randomness dominates, helping the agent discover options. Later, exploration dwindles, stabilizing behavior into exploitation of the learned policy.


The Central Idea: Action-Value Functions


The agent must answer: How good is it to take action a in state s? This is quantified by the Q-function:


Q(s,a) = expected cumulative reward from state s

          taking action a, then following the best policy


If Q(s, right) > Q(s, left), the agent knows moving right is more promising.



The Q-Learning Update



Q-learning is an off-policy algorithm: it learns the optimal Q-function even while exploring. The update rule:


Q(s,a) ← Q(s,a) + α [ r + γ * max_{a'} Q(s',a') − Q(s,a) ]


ASCII depiction:


               ┌──────────────────────────────┐

       │  Old estimate: Q(s,a)        

       └──────────────────────────────┘

                   

                   

 target = r + γ * max_a' Q(s',a')

                   

                   

 New Q(s,a) = Old + α * (target - Old)


The learning rate α controls how much new information overrides old beliefs. The discount factor γ values immediate vs. future rewards.



The Learning Curve: From Chaos to Competence


Engineers trust metrics. During training, we record:

Episode reward: total reward per episode.

Moving average: smoothed reward trend.

Evaluation: average greedy performance without exploration.


An ASCII illustration:


Reward

^

|            xxxx

|          xx    xx

|        xx        xxx

|    xxxx              xxx

+--------------------------------> Episodes


Early on, rewards oscillate around zero. As learning progresses, the curve climbs toward consistent positive values.



Seeing is Believing: Visual Replays


Numbers tell part of the story. But showing the agent’s behavior completes the picture. After training, we run the agent greedily and render each frame:


Frame 1:    Frame 2:    Frame 3:
            (ball)      (ball)
  (ball)                 .
             .           .
  [paddle]   [paddle]   [paddle]


The paddle moves into position, anticipating the ball’s landing. This visual confirmation reinforces the metrics.



Why This Matters for Software Engineers


So far, this may look like a toy. But the concepts generalize:

Adaptive resource allocation: RL can manage virtual machines in a cloud cluster, scaling resources up or down.

Traffic optimization: signals adjust in real-time, balancing flow.

Recommendation systems: explore new content but exploit user preferences.


These systems are more complex than Catch, but the feedback loop, the exploration–exploitation trade-off, and the Q-update are the same.



Lessons for Professionals

1. Reward shaping is design: Get the reward wrong, and the agent learns undesirable behavior.

2. Exploration is costly but necessary: Systems may underperform temporarily to discover better policies.

3. Policies emerge, not are programmed: RL shifts mindset from deterministic coding to probabilistic optimization.

4. Scaling requires function approximation: Tabular Q-learning suffices for Catch, but deep neural networks (DQN) are needed for larger spaces.




Beyond Tabular Q-Learning: The DQN Leap


Our tabular example does not scale. In modern practice, engineers use Deep Q-Networks (DQNs), where Q(s,a) is approximated by a neural network trained on replayed experiences. The architecture:


   State (pixels) ──► ConvNet ──► Fully connected ──► Q-values for actions


This is the method that allowed RL agents to reach human-level performance on Atari games. The essence is unchanged; the function approximator is more powerful.




Risks and Considerations


Professional deployments must consider:

Convergence: RL can diverge if hyperparameters are mis-set.

Safety: Exploration in real systems may cause harm.

Explainability: Policies learned by Q-functions may be hard to interpret.


Engineers must combine RL with simulation environments, guardrails, and monitoring.



Conclusion: From Arcade to Industry


By observing a paddle learn to catch a ball, we witness reinforcement learning in its purest form. The lesson for software engineers is not the specifics of tabular Q-tables but the architectural mindset: systems can be built to learn from feedback, balancing exploration and exploitation, updating expectations, and improving autonomously.


Today, it’s an arcade toy. Tomorrow, it could be your cloud infrastructure, your production pipeline, or your autonomous vehicle. The cycle is always the same:


observe state → choose action → receive reward → update Q → improve policy


Understanding this cycle equips engineers to participate in the next wave of adaptive software systems.




SOURCE CODE


„NORMAL“ VERSION


#!/usr/bin/env python3

"""

Arcade Catch + Q-learning (tabular) with live training curve and episode replays.


How to run:

  pip install numpy matplotlib



import math

import random

from collections import defaultdict, deque

from typing import Dict, Tuple, List


import numpy as np

import matplotlib.pyplot as plt



# =========================

# Environment: Catch Game

# =========================

class CatchEnv:

    """

    A tiny arcade-like game:

    - Discrete grid of width W and height H.

    - A ball spawns at the top row (y=H-1) at a random x and falls straight down.

    - A paddle sits on the bottom row (y=0) and can move LEFT (-1), STAY (0), or RIGHT (+1).

    - Episode ends when the ball leaves the grid; reward is +1 if paddle x == ball x, else -1.


    State representation: (ball_x, ball_y, paddle_x)

    """


    def __init__(self, width: int = 7, height: int = 7, paddle_speed: int = 1, seed: int = 0):

        self.W = width

        self.H = height

        self.paddle_speed = paddle_speed

        self.rng = random.Random(seed)

        self.reset()


    def reset(self) -> Tuple[int, int, int]:

        self.ball_x = self.rng.randrange(self.W)

        self.ball_y = self.H - 1

        self.paddle_x = self.W // 2

        return (self.ball_x, self.ball_y, self.paddle_x)


    def step(self, action: int) -> Tuple[Tuple[int, int, int], int, bool]:

        """

        Apply action in {-1, 0, +1}. Paddle is clamped to [0, W-1].

        Ball falls by one row. If ball leaves the grid, episode ends and reward is given.

        Returns: (next_state, reward, done)

        """

        self.paddle_x = int(np.clip(self.paddle_x + action * self.paddle_speed, 0, self.W - 1))

        self.ball_y -= 1


        done = False

        reward = 0

        if self.ball_y < 0:

            done = True

            reward = 1 if self.paddle_x == self.ball_x else -1


        state = (self.ball_x, max(self.ball_y, 0), self.paddle_x)

        return state, reward, done


    def render_frame(self) -> np.ndarray:

        """

        Return a 2D array with ball=2, paddle=1, empty=0 for visualization.

        """

        grid = np.zeros((self.H, self.W), dtype=int)

        if 0 <= self.ball_y < self.H:

            grid[self.ball_y, self.ball_x] = 2

        grid[0, self.paddle_x] = 1

        return grid



# =========================

# Q-learning Agent

# =========================

class QAgent:

    """

    Tabular Q-learning agent with epsilon-greedy exploration.

    Q is stored as a dict: state -> {action: value}

    """


    def __init__(

        self,

        width: int,

        height: int,

        actions: Tuple[int, ...] = (-1, 0, 1),

        alpha: float = 0.2,

        gamma: float = 0.98,

        eps_start: float = 1.0,

        eps_end: float = 0.05,

        eps_decay: float = 0.995,

        seed: int = 0,

    ):

        self.W = width

        self.H = height

        self.actions = actions

        self.alpha = alpha

        self.gamma = gamma

        self.eps = eps_start

        self.eps_end = eps_end

        self.eps_decay = eps_decay

        self.rng = random.Random(seed)


        self.Q: Dict[Tuple[int, int, int], Dict[int, float]] = defaultdict(

            lambda: {a: 0.0 for a in self.actions}

        )


    def policy(self, state: Tuple[int, int, int]) -> int:

        """Epsilon-greedy action selection."""

        if self.rng.random() < self.eps:

            return self.rng.choice(self.actions)

        qvals = self.Q[state]

        max_q = max(qvals.values())

        best_actions = [a for a, q in qvals.items() if q == max_q]

        return self.rng.choice(best_actions)


    def greedy_action(self, state: Tuple[int, int, int]) -> int:

        """Greedy (argmax) action selection."""

        qvals = self.Q[state]

        max_q = max(qvals.values())

        best_actions = [a for a, q in qvals.items() if q == max_q]

        return self.rng.choice(best_actions)


    def update(self, s: Tuple[int, int, int], a: int, r: int, s_next: Tuple[int, int, int], done: bool) -> None:

        """Tabular Q-learning update."""

        qsa = self.Q[s][a]

        target = r if done else r + self.gamma * max(self.Q[s_next].values())

        self.Q[s][a] = qsa + self.alpha * (target - qsa)


    def decay_epsilon(self) -> None:

        self.eps = max(self.eps_end, self.eps * self.eps_decay)



# =========================

# Training & Evaluation

# =========================

def train(

    env: CatchEnv,

    agent: QAgent,

    episodes: int = 2000,

    max_steps: int = 50,

    eval_every: int = 200,

    eval_episodes: int = 100,

) -> Tuple[List[float], List[float], List[Tuple[int, float]]]:

    """

    Train with epsilon-greedy Q-learning. Return (rewards, moving_avg, eval_scores).

    eval_scores is a list of (episode_index, avg_greedy_reward).

    """

    rewards: List[float] = []

    moving_avg: List[float] = []

    eval_scores: List[Tuple[int, float]] = []

    window = deque(maxlen=50)


    for ep in range(1, episodes + 1):

        s = env.reset()

        total = 0.0

        for _ in range(max_steps):

            a = agent.policy(s)

            s_next, r, done = env.step(a)

            agent.update(s, a, r, s_next, done)

            s = s_next

            total += r

            if done:

                break


        agent.decay_epsilon()

        rewards.append(total)

        window.append(total)

        moving_avg.append(sum(window) / len(window))


        if eval_every and (ep % eval_every) == 0:

            avg_r = evaluate(env, agent, episodes=eval_episodes, max_steps=max_steps)

            eval_scores.append((ep, avg_r))


    return rewards, moving_avg, eval_scores



def evaluate(env: CatchEnv, agent: QAgent, episodes: int = 50, max_steps: int = 50) -> float:

    """Evaluate the greedy policy (epsilon=0). Return average episode reward."""

    old_eps = agent.eps

    agent.eps = 0.0

    total = 0.0

    for _ in range(episodes):

        s = env.reset()

        ep_r = 0.0

        for _ in range(max_steps):

            a = agent.greedy_action(s)

            s, r, done = env.step(a)

            ep_r += r

            if done:

                break

        total += ep_r

    agent.eps = old_eps

    return total / episodes



# =========================

# Visualization

# =========================

def show_learning_curve(rewards: List[float], moving_avg: List[float], eval_scores: List[Tuple[int, float]]) -> None:

    plt.figure(figsize=(8, 4))

    plt.plot(rewards, label="Episode reward")

    plt.plot(moving_avg, label="Moving average (last 50)")

    if eval_scores:

        xs, ys = zip(*eval_scores)

        plt.scatter(xs, ys, label="Greedy eval avg", marker="x")

    plt.xlabel("Episode")

    plt.ylabel("Reward")

    plt.title("Q-learning on Catch: training progress")

    plt.legend()

    plt.tight_layout()

    plt.show()



def play_and_render(env: CatchEnv, agent: QAgent, episodes: int = 3, max_steps: int = 50) -> None:

    """

    Play a few episodes with the greedy policy and render frames using matplotlib.

    """

    old_eps = agent.eps

    agent.eps = 0.0


    for ep in range(1, episodes + 1):

        s = env.reset()

        frames: List[np.ndarray] = []

        rewards = 0

        for _ in range(max_steps):

            frames.append(env.render_frame())

            a = agent.greedy_action(s)

            s, r, done = env.step(a)

            rewards += r

            if done:

                frames.append(env.render_frame())

                break


        # Show frames one-by-one

        for i, frame in enumerate(frames):

            plt.figure(figsize=(3, 3))

            # invert Y so bottom row plots at bottom

            plt.imshow(frame[::-1, :], vmin=0, vmax=2)

            plt.xticks(range(env.W))

            plt.yticks(range(env.H))

            plt.title(f"Episode {ep} – frame {i+1}/{len(frames)}")

            plt.grid(True)

            plt.tight_layout()

            plt.show()


        # Episode result

        res = "CATCH!" if rewards > 0 else "MISS"

        plt.figure(figsize=(3, 0.6))

        plt.text(0.5, 0.5, f"Episode {ep} result: {res}", ha="center", va="center")

        plt.axis("off")

        plt.tight_layout()

        plt.show()


    agent.eps = old_eps



# =========================

# Main

# =========================

def main() -> None:

    W, H = 7, 7

    env = CatchEnv(width=W, height=H, seed=42)

    agent = QAgent(width=W, height=H, alpha=0.25, gamma=0.98, eps_start=1.0, eps_end=0.05, eps_decay=0.995)


    # Episodes ~= grid height + a little slack so ball reaches bottom

    episodes = 2000

    max_steps = H + 2


    rewards, moving_avg, eval_scores = train(

        env, agent, episodes=episodes, max_steps=max_steps, eval_every=200, eval_episodes=100

    )


    show_learning_curve(rewards, moving_avg, eval_scores)

    play_and_render(env, agent, episodes=3, max_steps=max_steps)



if __name__ == "__main__":

    main()




PIXEL VERSION DQN (accesses pixel directly)


#!/usr/bin/env python3

"""

Arcade Catch (pixels) + DQN (ConvNet) with experience replay and target network.

- Learns from raw grid images (1 channel) optionally stacked over K frames.

- Device auto-detects CUDA / Apple MPS / CPU.

- Visualizes training curve and plays greedy episodes after training.


Run:

  pip install numpy matplotlib torch

  python arcade_dqn.py


Tip: For better results, increase TRAIN_EPISODES and REPLAY_SIZE.

"""


import math

import random

import time

from collections import deque, namedtuple

from typing import Deque, List, Tuple


import numpy as np

import torch

import torch.nn as nn

import torch.optim as optim

import matplotlib.pyplot as plt



# =========================

# Environment: Catch (pixels)

# =========================

class CatchEnv:

    """

    A tiny arcade-like game rendered as pixels (H x W single-channel image with values {0,1,2}).

    - Ball falls downward; paddle moves left/stay/right.

    - Reward +1 for catch, -1 otherwise at episode end.

    - Observation: np.ndarray[H, W] with integers 0(empty),1(paddle),2(ball).

    """

    def __init__(self, width=7, height=7, paddle_speed=1, seed=0):

        self.W = width

        self.H = height

        self.paddle_speed = paddle_speed

        self.rng = random.Random(seed)

        self.reset()


    def reset(self):

        self.ball_x = self.rng.randrange(self.W)

        self.ball_y = self.H - 1

        self.paddle_x = self.W // 2

        return self._obs()


    def step(self, action:int):

        # action in {-1,0,+1} encoded as 0,1,2 externally; we'll map to -1,0,+1 here.

        mapped = [-1, 0, 1][action]

        self.paddle_x = max(0, min(self.W-1, self.paddle_x + mapped * self.paddle_speed))

        self.ball_y -= 1


        done = False

        reward = 0.0

        if self.ball_y < 0:

            done = True

            reward = 1.0 if self.paddle_x == self.ball_x else -1.0


        return self._obs(), reward, done


    def _obs(self):

        grid = np.zeros((self.H, self.W), dtype=np.float32)

        if 0 <= self.ball_y < self.H:

            grid[self.ball_y, self.ball_x] = 2.0

        grid[0, self.paddle_x] = 1.0

        return grid


    def render_frame(self):

        return self._obs()


    @property

    def action_space_n(self):

        return 3  # left, stay, right



# Optional wrapper to stack K consecutive frames -> encode motion

class FrameStack:

    def __init__(self, env: CatchEnv, k: int = 2):

        self.env = env

        self.k = k

        self.frames: Deque[np.ndarray] = deque(maxlen=k)


    def reset(self):

        obs = self.env.reset()

        self.frames.clear()

        for _ in range(self.k):

            self.frames.append(obs)

        return self._get_obs()


    def step(self, action:int):

        obs, r, done = self.env.step(action)

        self.frames.append(obs)

        return self._get_obs(), r, done


    def _get_obs(self):

        # shape: (k, H, W)

        return np.stack(list(self.frames), axis=0)


    def render_frame(self):

        return self.env.render_frame()


    @property

    def action_space_n(self):

        return self.env.action_space_n


    @property

    def H(self):

        return self.env.H


    @property

    def W(self):

        return self.env.W



# =========================

# DQN Model (ConvNet)

# =========================

class ConvDQN(nn.Module):

    def __init__(self, in_channels: int, height: int, width: int, num_actions: int):

        super().__init__()

        # Small conv net tailored for tiny 7x7 to ~15x15 inputs

        self.body = nn.Sequential(

            nn.Conv2d(in_channels, 32, kernel_size=3, padding=1),

            nn.ReLU(),

            nn.Conv2d(32, 64, kernel_size=3, padding=1),

            nn.ReLU(),

        )

        # compute conv output size

        with torch.no_grad():

            dummy = torch.zeros(1, in_channels, height, width)

            conv_out = self.body(dummy).view(1, -1).size(1)

        self.head = nn.Sequential(

            nn.Linear(conv_out, 128),

            nn.ReLU(),

            nn.Linear(128, num_actions),

        )


    def forward(self, x):

        x = self.body(x)

        x = x.view(x.size(0), -1)

        return self.head(x)



# =========================

# Replay Buffer

# =========================

Transition = namedtuple("Transition", ("state", "action", "reward", "next_state", "done"))


class ReplayBuffer:

    def __init__(self, capacity: int, device: torch.device):

        self.capacity = capacity

        self.device = device

        self.memory: Deque[Transition] = deque(maxlen=capacity)


    def push(self, *args):

        self.memory.append(Transition(*args))


    def __len__(self):

        return len(self.memory)


    def sample(self, batch_size: int):

        batch = random.sample(self.memory, batch_size)

        # Stack into tensors

        state = torch.tensor(np.stack([b.state for b in batch], axis=0), dtype=torch.float32, device=self.device)

        action = torch.tensor([b.action for b in batch], dtype=torch.long, device=self.device).unsqueeze(1)

        reward = torch.tensor([b.reward for b in batch], dtype=torch.float32, device=self.device).unsqueeze(1)

        next_state = torch.tensor(np.stack([b.next_state for b in batch], axis=0), dtype=torch.float32, device=self.device)

        done = torch.tensor([b.done for b in batch], dtype=torch.float32, device=self.device).unsqueeze(1)

        return state, action, reward, next_state, done



# =========================

# Utilities

# =========================

def get_device():

    if torch.cuda.is_available():

        return torch.device("cuda")

    # Apple MPS

    if hasattr(torch.backends, "mps") and torch.backends.mps.is_available():

        return torch.device("mps")

    return torch.device("cpu")



def epsilon_by_episode(ep, eps_start: float, eps_end: float, eps_decay: float):

    # Exponential decay over episodes

    return max(eps_end, eps_start * (eps_decay ** ep))



# =========================

# Training & Evaluation

# =========================

def select_action(model: nn.Module, state: np.ndarray, eps: float, n_actions: int, device: torch.device):

    if random.random() < eps:

        return random.randrange(n_actions)

    with torch.no_grad():

        s = torch.tensor(state, dtype=torch.float32, device=device).unsqueeze(0)  # (1,C,H,W)

        q = model(s)

        return int(q.argmax(dim=1).item())


def evaluate(env: FrameStack, model: nn.Module, device: torch.device, episodes: int = 50, max_steps: int = 50):

    model.eval()

    total = 0.0

    with torch.no_grad():

        for _ in range(episodes):

            s = env.reset()

            ep_r = 0.0

            for _ in range(max_steps):

                a = select_action(model, s, eps=0.0, n_actions=env.action_space_n, device=device)

                s, r, done = env.step(a)

                ep_r += r

                if done:

                    break

            total += ep_r

    model.train()

    return total / episodes


def plot_learning(reward_history, moving_avg, eval_points, eval_scores):

    plt.figure(figsize=(8,4))

    plt.plot(reward_history, label="Episode reward")

    plt.plot(moving_avg, label="Moving avg (50)")

    if eval_points:

        plt.scatter(eval_points, eval_scores, marker='x', label="Greedy eval avg")

    plt.xlabel("Episode")

    plt.ylabel("Reward")

    plt.title("DQN on Catch (pixels)")

    plt.legend()

    plt.tight_layout()

    plt.show()


def play_and_render(env: FrameStack, model: nn.Module, device: torch.device, episodes: int = 3, max_steps: int = 50):

    for ep in range(1, episodes+1):

        s = env.reset()

        frames = []

        rewards = 0.0

        for _ in range(max_steps):

            frames.append(env.render_frame())

            a = select_action(model, s, eps=0.0, n_actions=env.action_space_n, device=device)

            s, r, done = env.step(a)

            rewards += r

            if done:

                frames.append(env.render_frame())

                break


        for i, frame in enumerate(frames):

            plt.figure(figsize=(3,3))

            plt.imshow(frame[::-1, :], vmin=0, vmax=2)

            plt.xticks(range(env.W)); plt.yticks(range(env.H))

            plt.title(f"Episode {ep} – frame {i+1}/{len(frames)}")

            plt.grid(True); plt.tight_layout(); plt.show()


        plt.figure(figsize=(3,0.6))

        plt.text(0.5, 0.5, f"Episode {ep} result: {'CATCH!' if rewards>0 else 'MISS'}", ha='center', va='center')

        plt.axis('off'); plt.tight_layout(); plt.show()



def train_dqn(

    seed: int = 42,

    WIDTH: int = 7,

    HEIGHT: int = 7,

    FRAME_STACK: int = 2,

    TRAIN_EPISODES: int = 3000,

    MAX_STEPS: int = 10,  # ~H+3; ball reaches bottom

    BATCH_SIZE: int = 64,

    REPLAY_SIZE: int = 5000,

    START_LEARNING_AFTER: int = 500,

    GAMMA: float = 0.99,

    LR: float = 1e-3,

    TARGET_UPDATE_EVERY: int = 200,   # steps

    EPS_START: float = 1.0,

    EPS_END: float = 0.05,

    EPS_DECAY: float = 0.995,         # per episode

    EVAL_EVERY: int = 200,

    EVAL_EPISODES: int = 100,

    SAVE_PATH: str = "dqn_catch.pt",

):

    random.seed(seed); np.random.seed(seed); torch.manual_seed(seed)

    base_env = CatchEnv(width=WIDTH, height=HEIGHT, seed=seed)

    env = FrameStack(base_env, k=FRAME_STACK)


    device = get_device()

    print(f"[INFO] Device: {device}")


    in_channels = FRAME_STACK

    n_actions = env.action_space_n


    policy_net = ConvDQN(in_channels, HEIGHT, WIDTH, n_actions).to(device)

    target_net = ConvDQN(in_channels, HEIGHT, WIDTH, n_actions).to(device)

    target_net.load_state_dict(policy_net.state_dict())

    target_net.eval()


    optimizer = optim.Adam(policy_net.parameters(), lr=LR)

    replay = ReplayBuffer(REPLAY_SIZE, device=device)


    reward_history: List[float] = []

    moving_avg: List[float] = []

    eval_points: List[int] = []

    eval_scores: List[float] = []

    ma_window: Deque[float] = deque(maxlen=50)


    global_step = 0

    for ep in range(1, TRAIN_EPISODES+1):

        s = env.reset()

        total_r = 0.0


        eps = epsilon_by_episode(ep, EPS_START, EPS_END, EPS_DECAY)


        for t in range(MAX_STEPS):

            a = select_action(policy_net, s, eps, n_actions, device)

            s_next, r, done = env.step(a)


            replay.push(s, a, r, s_next, float(done))

            s = s_next

            total_r += r

            global_step += 1


            # Learn

            if len(replay) >= max(BATCH_SIZE, START_LEARNING_AFTER):

                state, action, reward, next_state, done_mask = replay.sample(BATCH_SIZE)


                # Q(s,a)

                q_values = policy_net(state).gather(1, action)  # [B,1]


                with torch.no_grad():

                    # Double DQN trick (optional here): act by policy net, evaluate by target net

                    next_actions = policy_net(next_state).argmax(dim=1, keepdim=True)

                    next_q = target_net(next_state).gather(1, next_actions)

                    target = reward + (1.0 - done_mask) * GAMMA * next_q


                loss = nn.SmoothL1Loss()(q_values, target)


                optimizer.zero_grad()

                loss.backward()

                nn.utils.clip_grad_norm_(policy_net.parameters(), 5.0)

                optimizer.step()


            # Target network update

            if (global_step % TARGET_UPDATE_EVERY) == 0:

                target_net.load_state_dict(policy_net.state_dict())


            if done:

                break


        reward_history.append(total_r)

        ma_window.append(total_r)

        moving_avg.append(sum(ma_window)/len(ma_window))


        if (ep % EVAL_EVERY) == 0:

            avg = evaluate(env, policy_net, device, episodes=EVAL_EPISODES, max_steps=MAX_STEPS)

            eval_points.append(ep)

            eval_scores.append(avg)

            print(f"[EVAL] Ep {ep} avg reward (greedy): {avg:.3f}  eps={eps:.3f}")


    # Save model

    torch.save({"model": policy_net.state_dict(),

                "in_channels": in_channels,

                "H": HEIGHT,

                "W": WIDTH,

                "n_actions": n_actions}, SAVE_PATH)

    print(f"[INFO] Saved model to {SAVE_PATH}")


    # Plots and playback

    plot_learning(reward_history, moving_avg, eval_points, eval_scores)

    play_and_render(env, policy_net, device, episodes=3, max_steps=MAX_STEPS)



def main():

    # Defaults are tuned for demo speed. For stronger learning bump TRAIN_EPISODES to 10k+.

    train_dqn(

        TRAIN_EPISODES=3000,

        REPLAY_SIZE=8000,

        START_LEARNING_AFTER=500,

        MAX_STEPS=10,     # ~height + few steps

        FRAME_STACK=2,

        TARGET_UPDATE_EVERY=200,

        EVAL_EVERY=300,

        EVAL_EPISODES=100,

        LR=1e-3,

    )



if __name__ == "__main__":

    main()