Reinforcement Learning

This course presents the main concepts and algorithms of reinforcement learning:

• Markov decision process
• Dynamic programming (policy iteration, value iteration)
• Online control (Q-learning, Monte-Carlo Tree Search)
• Bandit algorithms

Teacher: Thomas Bonald

References:

• Course of Olivier Sigaud
• Course of David Silver
• Book of Richard Sutton and Andrew Barto