WHAT WE ARE DOING

WHY WE ARE DOING IT

We will tune an AI bot's parameters to give it a distinct personality, observe how it behaves in a dungeon maze, and document your findings in a field guide entry.

To understand reinforcement learning by watching it happen live in a game, then connecting it to real AI in the world.

؜2nd

3rd

4th

Play First, Think Later

Learn about Reinforced Learning

Design Your Bot

Build Your Field Guide

ACTIVITY STEPS

1st

5th

Build Your Own Game

Reinforcement learning is a type of machine learning where an AI learns by trying things, seeing what happens, and adjusting its behavior based on rewards and penalties.

The AI figures it out by exploring, making mistakes, and gradually doing better. It learns the same way you might learn a new video game: by playing, failing, and slowly figuring out what works.

REINFORCEMENT LEARNING

1st

Go to

When the game loads, select the Reinforcement Learning mini-game from the main menu NOT the Supervised Learning one.

Design Your Bot

؜Things ArtBot wants to find.

When ArtBot collects a reward, it receives positive feedback and learns that the path it took was a good choice.

1. Encounter object
2. Collect rewards to earn points.
3. Positive reward = "Do this again!"

The more rewards ArtBot finds, the better it learns where to go.

Game Rewards +

؜Traps

Things ArtBot should avoid.

If ArtBot steps on a trap, it receives negative feedback and learns that this path is not a good choice.

1. Encounter object
2. Lose points.
3. Negative reward = "Avoid this path."
4. After enough experience, ArtBot will try to avoid traps.

Game Traps -

؜The final goal.

1. Once ArtBot has collected rewards and avoided traps, it should find the exit as efficiently as possible.

Game Settings: Exit

1. Duration: Long: More time to learn and improve. Short: Less time, so it may not find the best path.
2. Exploration: High: Tries lots of new paths. Low: Sticks to routes that already worked.
3. Experience: High: Remembers where rewards were found. Low: Often forgets and starts fresh.
4. Learning Rate: High: Learns from rewards right away. Low: Needs to find the same reward several times before it remembers.

Game Settings

BOT EXAMPLES

Here are two Examples of different settings for the game's level one

Based on what we learned, how would you describe each of those AI bots?

Exploration: High

Learning Rate: Low

Exploration: Low

Learning Rate: High

2nd

Design Your Bot

1. ؜Choose of one the 5 profiles to give your bot.

1. Set the parameters to match your creature's profile.

1. Run 5-10 episodes. Take notes on what you observe.

1. Compare: did the bot behave the way you predicted? What surprised you?

BOT PERSONALITIES

Cautious

Reckless

Explorer

Efficient

Random

؜We will be choosing one of these personalities to try to give to our bot

Plays it safe.

Rarely strays from the first path that worked.

Tries everything at full speed.

Unstable and impulsive.

Always wandering. Curious but never commits.

Steady improvement. Finds the best path and locks it in.

Pure chaos.

No learning, no memory, no strategy.

ASK YOURSELF

Cautious

Reckless

Explorer

Efficient

Random

how would each of those personalities affect the bot's learning and exploration slider?

Plays it safe.

Rarely strays from the first path that worked.

Tries everything at full speed.

Unstable and impulsive.

Always wandering. Curious but never commits.

Steady improvement. Finds the best path and locks it in.

Pure chaos.

No learning, no memory, no strategy.

For your bot, ask:

would exploration be high or low here?

what about learning?

would it care about a reward or a punishment?

Solutions

Cautious

Exploration

Explorer

Reckless

Efficient

Random

؜Learning Rate

Memory

Duration

low

mid

high

mid

low

mid

high

mid

mid

high

high

high

low

high

high

high

high

low

low

mid

Gemini, Copilot, and Claude are AI assistants that support different types of creative and technical work. They can understand natural-language instructions, answer questions, generate and edit text, analyze information and files, help write and debug code, and support the creation of digital projects.

Each tool has different strengths and integrations, but all can help users move from an idea → research → creation → testing → refinement more efficiently.

What is Claude, Gemini, Copilot?

3rd

You will build a game using AI and your chosen theme.

For example, if your theme is Space, your bot might collect lunar resources (rewards), avoid space hazards (traps), and move through a lunar environment.

Think about how your theme can shape the game’s characters, assets, obstacles, setting, and overall mission.

Turn it into a Game!

4th

Use the following prompt to AI. It will generate a working HTML maze game. You can open it in any browser.

Turn it into a Game!

YOUR PROMPT: copy, fill in the brackets, paste into AI

Make me a simple platformer game where I control a creature and try to reach the exit.

The creature is called "[YOUR CREATURE NAME]".

The creature moves and reacts based on this personality: [CAUTIOUS / RECKLESS / EXPLORER / EFFICIENT / RANDOM].

Use these observed behaviors to shape the game:

1. It [paste one behavior]
2. It [paste another behavior]

Add this twist to the level: [paste your challenge question and turn it into a game mechanic].

Include:

1. Arrow keys or WASD controls
2. Platforms, obstacles, and an exit
3. An episode or level number
4. The player’s score
5. A restart button
6. A simple win or game-over screen

Create everything in one file using HTML, CSS, and JavaScript only.

5th

Turn it into a Game!

Paste your prompt into AI and modify:

You can have a maze or platformer game

Behaviors: Runs fast, moves slowly, hides from enemies, collects everything, avoids water

Twist: Double jump, wall climb, temporary invisibility, slow-motion power, teleport short distances

Save it as [XXX].html and open in your browser.

Example

1. Get/download the HTML, CSS, JavaScript, and any assets from the AI tool.
2. Create a new repository on GitHub and upload the app files.
3. Go to Repository → Settings → Pages → Deploy from a branch → main → /root → Save.
4. GitHub Pages gives you a public link that students can open on a phone or computer.
5. (For a simple project, you can even keep everything in one index.html file, which makes the Claude/Gemini → GitHub workflow much easier.)

AI Tool → HTML → GitHub → GitHub Pages → Live App

6th

Publish it!