Reinforcement Learning
author:
Satinder Singh,
Public Policy and Sociology, University of Michigan
Description
MDPs/VI,
Q learning (w/ proof),
TD(lambda),
Function approximation,
options,
PSRs
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| Slides | |
| 0:01 | Reinforcement Learning: A Tutorial |
| 2:51 | Outline |
| 4:00 | RL is Learning from Interaction |
| 6:21 | RL (another view) |
| 9:29 | Key Ideas in RL |
| 10:23 | Demos... |
| 10:27 | Demos... |
| 11:16 | Demos... |
| 12:27 | Keepaway Soccer (Stone & Sutton) |
| 13:01 | Keepaway Soccer (Stone & Sutton) |
| 13:04 | Tetris Demo |
| 14:17 | History & Place |
| 14:29 | Place |
| 16:57 | (Partial) History |
| 17:56 | (Partial) History... |
| 18:17 | RL and Machine Learning |
| 20:11 | (Partial) List of Applications |
| 21:10 | List of Conferences and Journals |
| 21:12 | Model of Agent-Environment Interaction |
| 23:07 | Markov Decision Processes |
| 24:48 | MDP Preliminaries |
| 31:12 | MDP Preliminaries... |
| 33:12 | Bellman Optimality Equations |
| 37:51 | Bellman Optimality Equations |
| 42:00 | Graphical View of MDPs |
| 43:04 | Planning & Learning |
| 43:22 | Planning in MDPs |
| 47:01 | Planning in MDPs |
| 48:01 | Planning in MDPs |
| 49:49 | Convergence of Value Iteration |
| 49:56 | Proof of the DP contraction |
| 54:13 | Learning in MDPs |
| 57:21 | Indirect Methods for Learning in MDPs |
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