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Structure learning in ergodic factored mdps without knowledge of the transition function’s in-degree (2011)
| Content Provider | CiteSeerX |
|---|---|
| Author | Chakraborty, Doran Stone, Peter |
| Description | In Proceedings of the Twenty-eighth International Conference on Machine Learning (ICML 2011 |
| Abstract | This paper introduces Learn Structure and Exploit RMax (LSE-RMax), a novel model based structure learning algorithm for ergodic factored-state MDPs. Given a planning horizon that satisfies a condition, LSE-RMax provably guarantees a return very close to the optimal return, with a high certainty, without requiring any prior knowledge of the in-degree of the transition function as input. LSE-RMax is fully implemented with a thorough analysis of its sample complexity. We also present empirical results demonstrating its effectiveness compared to prior approaches to the problem. 1. |
| File Format | |
| Publisher Date | 2011-01-01 |
| Access Restriction | Open |
| Subject Keyword | Ergodic Factored-state Mdps Exploit Rmax Sample Complexity Thorough Analysis Planning Horizon High Certainty Present Empirical Result Novel Model Learn Structure Optimal Return Prior Knowledge Transition Function Ergodic Factored Mdps |
| Content Type | Text |
| Resource Type | Proceeding Conference Proceedings Article |
