Institutional Repository
Technical University of Crete
Technical University of Crete
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| URI | http://purl.tuc.gr/dl/dias/2F95F669-B215-44BD-90AF-6176BD490AA9 | - |
| Identifier | http://arxiv.org/ftp/arxiv/papers/1301/1301.0580.pdf | - |
| Language | en | - |
| Extent | 10 pages | en |
| Title | Value function approximation in zero–sum Markov games | en |
| Creator | Lagoudakis Michael | en |
| Creator | Λαγουδακης Μιχαηλ | el |
| Creator | Parr,R. | en |
| Content Summary | This paper investigates value function approximation in the context of zero-sum Markov games, which can be viewed as a generalization of the Markov decision process (MDP) framework to the two-agent case. We generalize error bounds from MDPs to Markov games and describe generalizations of reinforcement learning algorithms to Markov games. We present a generalization of the optimal stopping problem to a two-player simultaneous move Markov game. For this special problem, we provide stronger bounds and can guarantee convergence for LSTD and temporal difference learning with linear value function approximation. We demonstrate the viability of value function approximation for Markov games by using the Least squares policy iteration (LSPI) algorithm to learn good policies for a soccer domain and a flow control problem. | en |
| Type of Item | Πλήρης Δημοσίευση σε Συνέδριο | el |
| Type of Item | Conference Full Paper | en |
| License | http://creativecommons.org/licenses/by/4.0/ | en |
| Date of Item | 2015-11-13 | - |
| Date of Publication | 2002 | - |
| Subject | Artificial Intelligence | en |
| Bibliographic Citation | M.G. Lagoudakis and R. Parr. (2002, Aug.). Value function approximation in zero–sum Markov games. [Online]. Available: http://arxiv.org/ftp/arxiv/papers/1301/1301.0580.pdf | en |
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