Reinforcement Learning
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Lecture Notes
- Probability, Conditional Probability, Bayes’ theorem, Likelihood, MLE, and MAP
- Information, Entropy, Cross Entropy, KL Divergence
- Markov Decision Problem, Value Function & Q-Value
- Bellman Equation, Dynamic Programming
- Monte-Carlo Learning, SARSA, Q-Learning
- Deep Q-Learning
- Policy Gradient, REINFORCE, A2C
- TRPO & PPO
- Inverse Reinforcement Learning
- Papers
- Basic IRL, Ng & Russel, 2000
- Apprenticeship Learning via IRL, Abbeel & Ng, 2004
- Bayesian IRL, Ramachandran & Amir, 2007
- Maximum Entropy IRL, Ziebart et al., 2008
- Maximum Causal Entropy IRL, Ziebart et al., 2010
- Maximum Entropy Deep IRL, Wulfmeier et al., 2016
- Unity Machine Learning Agents Toolkit
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Laboratory
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Movie Clips
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Papers
