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Analyzing and Escaping Local Optima in Planning as Inference for Partially Observable Domains
Published on Nov 30, 20112718 Views
Planning as inference recently emerged as a versatile approach to decision-theoretic planning and reinforcement learning for single and multi-agent systems in fully and partially observable domains wi
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Chapter list
Analyzing and Escaping Local Optima in Planning as Inference for Partially Observable Domains00:00
Introduction00:14
Outline01:46
POMDP Graphical Representation02:24
Policy Optimization02:54
Finite State Controllers03:38
Graphical Model - 104:32
Graphical Model - 205:39
Graphical Model - 306:10
Graphical Model - 406:44
Planning as Inference07:25
Local Optima07:51
EM details08:23
Local Optima Conditions - 109:11
Optimality09:46
Local Optima Conditions - 212:47
Multi-step forward search13:20
Node Splitting14:22
Complexity15:15
Experiments - 115:56
Experiments - 218:11
Experiments - 318:42
Conclusion20:51