Introduction to Reinforcement Learning

Dive into the world of Reinforcement Learning (RL) with this comprehensive video tutorial. Explore the fundamental theory behind RL and learn how to implement it using Farama Foundation Gymnasium and Stable Baselines3 in Python. Follow along as the instructor demonstrates training an AI agent to solve the classic cartpole control theory problem. Gain insights into the RL process, including environment-agent interactions, Markov decision processes, and Bellman equations. Discover the differences between model-based and model-free algorithms, on-policy and off-policy approaches, and discrete vs. continuous action and observation spaces. Get hands-on experience setting up a Gymnasium environment and training a Deep Q-Network (DQN) agent. Conclude with a challenge to apply your newfound knowledge to the inverted pendulum problem, and explore additional resources for further learning in this exciting field of machine learning.

- Intro
- History of reinforcement learning
- Environment and agent interaction loop
- Gymnasium and Stable Baselines3
- Hands-on: how to set up a gymnasium environment
- Markov decision process
- Bellman equation for the state-value function
- Bellman equation for the action-value function
- Bellman optimality equations
- Exploration vs. exploitation
- Recommended textbook
- Model-based vs. model-free algorithms
- On-policy vs. off-policy algorithms
- Discrete vs. continuous action space
- Discrete vs. continuous observation space
- Overview of modern reinforcement learning algorithms
- Q-learning
- Deep Q-network DQN
- Hands-on: how to train a DQN agent
- Usefulness of reinforcement learning
- Challenge: inverted pendulum
- Conclusion