Reinforcement Learning Using Approximate Belief States
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Daphne Koller | Ronald Parr | Andres C. Rodriguez | Andrés C. Rodríguez | Ronald E. Parr | D. Koller
[1] Karl Johan Åström,et al. Optimal control of Markov processes with incomplete state information , 1965 .
[2] Edward J. Sondik,et al. The Optimal Control of Partially Observable Markov Processes over a Finite Horizon , 1973, Oper. Res..
[3] Andrew McCallum,et al. Overcoming Incomplete Perception with Utile Distinction Memory , 1993, ICML.
[4] Stuart J. Russell,et al. Approximating Optimal Policies for Partially Observable Stochastic Domains , 1995, IJCAI.
[5] Leslie Pack Kaelbling,et al. Learning Policies for Partially Observable Environments: Scaling Up , 1997, ICML.
[6] Corso Elvezia. Hq-learning: Discovering Markovian Subgoals for Non-markovian Reinforcement Learning , 1996 .
[7] Marco Wiering,et al. HQ-Learning: Discovering Markovian Subgoals for Non-Markovian Reinforcement Learning , 1996 .
[8] Michael L. Littman,et al. Algorithms for Sequential Decision Making , 1996 .
[9] John Loch,et al. Using Eligibility Traces to Find the Best Memoryless Policy in Partially Observable Markov Decision Processes , 1998, ICML.
[10] Xavier Boyen,et al. Tractable Inference for Complex Stochastic Processes , 1998, UAI.
[11] A. Cassandra,et al. Exact and approximate algorithms for partially observable markov decision processes , 1998 .
[12] Craig Boutilier,et al. Decision-Theoretic Planning: Structural Assumptions and Computational Leverage , 1999, J. Artif. Intell. Res..