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- Intro
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Classroom Contents
Introduction to Reinforcement Learning
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- 1 - Intro
- 2 - History of reinforcement learning
- 3 - Environment and agent interaction loop
- 4 - Gymnasium and Stable Baselines3
- 5 - Hands-on: how to set up a gymnasium environment
- 6 - Markov decision process
- 7 - Bellman equation for the state-value function
- 8 - Bellman equation for the action-value function
- 9 - Bellman optimality equations
- 10 - Exploration vs. exploitation
- 11 - Recommended textbook
- 12 - Model-based vs. model-free algorithms
- 13 - On-policy vs. off-policy algorithms
- 14 - Discrete vs. continuous action space
- 15 - Discrete vs. continuous observation space
- 16 - Overview of modern reinforcement learning algorithms
- 17 - Q-learning
- 18 - Deep Q-network DQN
- 19 - Hands-on: how to train a DQN agent
- 20 - Usefulness of reinforcement learning
- 21 - Challenge: inverted pendulum
- 22 - Conclusion
