RAICE 🤖🏎️🏁

(STILL ON DEVELOPMENT)

INTRODUCTION

RL agents are trained on a custom made racing game. The goal is train multiple algorithms to race on the same tracks and see which one is the best across all F1 tracks.

RACING CAR-ALGORITHMS

• Blue Cars: Correspond to Policy-Based Algorithms (e.g., Proximal Policy Optimization, PPO). These agents learn a policy directly by optimizing the expected return, focusing on selecting the best action in each state.

• Green Cars: Represent Value-Based Algorithms (e.g., Deep Q-Learning, DQN). These agents learn to estimate the value of actions and states, aiming to improve decision-making based on long-term expected rewards.

• Both Colors (Blue + Green): These cars utilize Hybrid Algorithms that combine both policy-based and value-based approaches, such as Actor-Critic methods, where one part learns the policy and another learns the value function.

• White Cars: Indicate the use of a Genetic Algorithm. These cars evolve over time through selection, mutation, and crossover, mimicking natural evolution to optimize their behavior.

RAICE EXAMPLE

Videograbacion.2024-09-21.15.07.26.webm

CURRENT MAPS

TRACK	IMAGE	READY
🇧🇭 BAHREIN		YES

Next Steps

• Train PPO (Proximal Policy Optimization)
• Train A2C (Advantage Actor-Critic)
• Test all cars on the test track
• Create a reinforcement learning course explaining the project and algorithms
• Add more tracks for evaluation
• Create a competition using all available tracks
• Add a track/circuit creator tool