1. UAM RL

Welcome to the UAM Reinforcement Learning Organization.

1.1. About

This organization is dedicated to reinforcement learning research, implementations, and educational resources.

1.2. Research Areas

Our research focuses on:

• Deep Reinforcement Learning: Applying deep neural networks to RL problems
• Policy Optimization: Developing efficient policy gradient methods
• Multi-Agent RL: Studying interactions between multiple learning agents
• Sample Efficiency: Improving data efficiency in RL algorithms

1.3. Mathematical Foundations

The core of reinforcement learning revolves around the Bellman equation:

Where:

• is the value function
• is the discount factor
• is the reward function

The expected return for a policy can be written as:

1.4. Get Involved

Visit our GitHub organization at https://github.com/uam-rl to explore our projects and contribute to our research.