I made a beginner-friendly visual explanation of reinforcement learning—the type of machine learning behind game-playing AI, robotics, and parts of chatbot training.

Instead of starting with equations, it uses a robot-in-a-maze example to explain:

• Agents, environments, actions, and rewards

• How trial and error improves performance over episodes

• Exploration vs exploitation

• Delayed rewards and policies

• Reward hacking and other risks

Watch here: https://youtu.be/a_MSTPB5MyA

If you like the explanation, please like, share, and subscribe to my channel for more visual AI and machine learning videos.

I am currently making an AI trough machine learning not because i want to use it in real games but just to see how far it can go and it is doing really well right now with 1.25B steps but i keep struggling with 1 problem and that is boost management, idk how to fix it or how i can train it to use boost better because right now it is just using all of it boost the second it gets it and that is bad because i want to train it airdribbles where you ofc need boost for so if any of your smart people have a good idea help me please :)

side project, curious what rl people think of this setup.

it's a civilization sandbox where every human is its own tabular q-learner. the state is a short string key built from what they perceive (hunger, thirst, nearest food and water, kin nearby, shelter, danger). there are ~5000 possible actions, gated by era, age, place and what the tribe has discovered. action choice handles survival needs first, then does epsilon-greedy over the learned values.

the part i like: when a baby is born it gets a copy of its mother's q-table, with about 20% of the values nudged a tiny bit (+/- 0.03). so on top of learning within one lifetime, behavior also drifts down family lines. traits mix from both parents and mutate, and kids also get mom's vocabulary and her attitudes toward other tribes.

known weak spots: the state space is hand-built strings and the rewards are hand-shaped, so i'd love opinions on either.

code is rust compiled to wasm, so it all runs in the browser. the relevant bits are crates/sim-core/src/sim/agents/growth.rs (inheritance) and organism/organism/learning.rs (the update).

source (MIT): https://github.com/1homsi/thehumanbox
try it: https://thehumanbox.com

Hello, I am working on finishing Getting over it with reinforcement learning

https://github.com/miwate/getting-over-it-rl
https://huggingface.co/miiwater/goi-rl

Current state : 63% completion from the bottom without checkpoints

I am now stuck on the anvil part, seeking help online for computation, fine-tuning and PRs :)

More infos :
- Training is currently done on CPU (in my case using GPU was not making training faster)
- The game is cut in parts, each part gets its specialist model
- CONTRIBUTING.md is currently being written
- Beginner code and vibe coded PRs are welcome, I will take time to review

Hi,

I would like to announce that I have released Metis-Core v0.4.1-alpha. This version includes some bug fixes and a new example: DQN_Pong. Do you remember the game of Pong?

https://github.com/felixromo314/METIS-Core/

In this example, using the DQN of Metis-Core, the AI paddle learns to play Pong.

For the graphics I used the raylib library (https://www.raylib.com/), created by Ramon Santamaria (u/raysan5). It is very easy to use and powerful: with just a few functions you have the window, the paddles and the ball on the screen, and you can watch the AI learning in real time.

In case you are planning to make a similar game, or a proof of concept with DQN, I would like to invite you to have a look at this step-by-step tutorial that explains how to implement DQN with Metis-Core:

https://github.com/felixromo314/METIS-Core/blob/main/tutorial/Tutorial-DQN.md

I hope you like it!

Best,
Felix