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Syllabus - OpenAI Project

Posted on:June 8, 2018 at 08:00 AM

OpenAI Scholars 2018 Reinforcement Learning Syllabus

Primary Resources

Week 1, Jun 4: Markov Decision Processes

Topics: Dynamic Programming (Value iteration, Policy iteration, and Q-learning)

Week 2, Jun 11 Monte Carlo Methods

Topics: Use Blackjack to implement first-visit or every-visit MC prediction

Week 3, Jun 18 Imitation Learning with Mujoco

Week 4, Jun 25 Policy Gradients

Topics: TD (Temporal Difference), use Cartpole and Humanoid for Policy Gradients

Week 5, Jul 2 Deep Q Learning, DQN, Rainbow

Week 6, Jul 9 Model-based RL

Week 7, Jul 16 Advanced Policy Gradients

Topics: Advanced Policy Gradients: Natural Policy, PPO (Use Roboschool instead of Mujoco license)

Week 8, Jul 23 Inverse RL

Topics: GAIL