Least Squares Temporal Difference Methods: An Analysis under General Conditions
We consider approximate policy evaluation for finite state and action Markov decision processes (MDP) with the least squares temporal difference (LSTD) algorithm, LSTD($\lambda$), in an exploration-enhanced learning context, where policy costs are computed from observations of a Markov chain differe...
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| Format: | Article |
| Language: | en_US |
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Society for Industrial and Applied Mathematics
2013
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| Online Access: | http://hdl.handle.net/1721.1/77629 |