Reinforcement Learning Agent
Teaching AI to learn through interaction and rewards
About This Project
This project involves developing a reinforcement learning agent using algorithms such as Q-learning to navigate and excel in simulation environments. The agent is designed to play simulation games like CartPole or similar OpenAI Gym/AI environments, learning through trial and error to maximize rewards. The project includes comprehensive performance analysis across different hyperparameter configurations to identify optimal settings and understand the dynamics of the learning process.
Core Concepts
- Reinforcement learning fundamentals
- Q-learning algorithm implementation
- Policy and value function approximation
- Exploration vs. exploitation strategies
- Hyperparameter tuning for RL agents
- Performance evaluation metrics
Key Knowledge/Skills
- Reinforcement learning algorithms (RL & DL theory)
- Policy gradient methods
- Environment simulation with Gym/MuJoCo
- Performance optimization techniques
- Model evaluation and comparison
- State-action-reward system design
Coursework Covered
Reinforcement Learning (Advanced ML & RL Theory)