Solving POMDPs with Levin Search and EIRA

By Marco A. Wiering, J. Urgen Schmidhuber. 1996

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Citations in the corpus

Deep learning in neural networks: An overview (2014, background): “…& Jordan, 1995; Kaelbling et al., 1995; Kimura, Miyazaki, & Kobayashi, 1997; Lin, 1993; Littman, Cassandra, & Kaelbling, 1995; McCallum, 1996; Otsuka, Yoshimoto, & Doya, 2010; Ring, 1991, 1993, 1994; Schmidhuber, 1990d, 1991c; Teller, 1994; Wiering & Schmidhuber, 1996, 1998a; Williams, 1992a).”
On Learning to Think: Algorithmic Information Theory for Novel Combinations of Reinforcement Learning Controllers and Recurrent Neural World Models (2015, background): “This triggered work on partially observable Markov decision problems (POMDPs) [223, 222, 227, 204, 205, 206, 316, 148, 278, 122, 152, 25, 114, 160, 126, 308, 309, 183].”
Optimal Ordered Problem Solver (2002, positive): “Subsequent Lsearch es for new problems then use the adjusted P , etc. Schmidhuber et al. (1997b) and Wiering and Schmidhuber (1996) used a nonuniversal variant of this approach to solve reinforcement learning (RL) tasks in partially observable environments unsolvable by traditional RL algorithms.”
Shifting Inductive Bias with Success-Story Algorithm, Adaptive Levin Search, and Incremental Self-Improvement (1997, unknown): “See also (Wiering & Schmidhuber, 1996).”
Incremental self-improvement for life-time multi-agent reinforcement learning (1996, background): “Or, as in (Wiering and Schmidhuber , 1996), actions may be calls of Levin search (Levin, 1973), a theoretically optimal algorithm for a wide variety of non-incremental search problems.”
HQ-Learning (1997, methodology): “Wiering and Schmidhuber (1996) also extend LS to obtain anincremental method for generalizing from previous experience (\adaptive LS").”
Simple Principles of Metalearning (1996, unknown): “See also (Wiering and Schmidhuber, 1996).”
Probabilistic Incremental Program Evolution (1997, methodology): “PIPE is partly inspired by recent work on reinforcement learning with probabilistic programming languages (Schmidhuber, 1994; Wiering & Schmidhuber, 1996b; Zhao & Schmidhuber, 1996; Schmidhuber, Zhao, & Schraudolph, 1997a; Schmidhuber, Zhao, & Wiering, 1997b).”
Reinforcement Learning with Self-Modifying Policies (1998, methodology): “Our adaptive LS extension (ALS) [15, 10] does use experience to modify LS' underlying probability distribution.”
A GENERAL METHOD FOR MULTI-AGENT REINFORCEMENT LEARNING IN UNRESTRICTED ENVIRONMENTS (1996, background): “See (Schmidhuber, 1995; Zhao and Schmidhuber, 1996; Wiering and Schmidhuber, 1996) for more recent work along these lines.”