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Winter 2020: Reinforcement Learning

Winter 2020: Reinforcement Learning

Course No:
EECS 598-002
Credit Hours:
3 credits
Instructor:
Lei Ying
Prerequisites:
EECS 502 or equivalent

This course covers fundamental theories and principles of reinforcement learning. Topics to be covered include:

  1. Dynamic programming and the principle of optimality
  2. Multi-armed bandit: epsilon-greedy, Upper Confidence Bound (UCB) algorithm, Thompson Sampling
  3. Markov chains and Markov Decision Process (MDP)
  4. Value iteration, policy iteration, and LP formulation
  5. Q-Learning: Model-based and model-free
  6. Linear function approximation and deep reinforcement learning
  7. Temporal-difference learning
  8. SARSA
  9. Policy gradient algorithm and variance reduction
  10. The ODE methods and convergence analysis
More info (pdf)