Resources for Reinforcement Learning: Theory and Practice
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Week 0: Class Overview, Introduction
Week 1: Introduction and Evaluative Feedback
Week 2: MDPs and Dynamic Programming
Week 3: Monte Carlo Methods and Temporal Difference Learning
Week 4: Multi-Step Bootstrapping and Planning
Week 5: Approximate On-policy Prediction and Control
Week 6: Approximate Off-policy Methods and Eligibility Traces
Week 7: Applications and Case Studies
Week 8: Efficient Model-Based Exploration
Week 9: Abstraction: Options and Hierarchy
Week 10: Multiagent RL
Week 11: Policy Gradient Methods
Week 12: Inverse RL and Transfer Learning
Week 13: Deep RL
Reinforcement learning with unsupervised auxiliary tasks from Deep Mind includes some action conditional learning.An explanation of LSTMs.The Recurrent Temporal Restricted Boltzmann Machine 1 0