Introduction to Reinforcement Learning

An introduction to reinforcement learning covering its core theory and algorithm types. Learners set up Gymnasium and Stable Baselines3 in Python, then train a Deep Q-Network to balance a simulated cart-pole.

Syllabus

- 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