Evolutionary Search, Stochastic Policies with Memory, and Reinforcement Learning with Hidden State
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
Direct Policy Search using Paired Statistical Tests
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
Learning Policies with External Memory
ICML '99 Proceedings of the Sixteenth International Conference on Machine Learning
Reinforcement learning with selective perception and hidden state
Reinforcement learning with selective perception and hidden state
Algorithms for sequential decision-making
Algorithms for sequential decision-making
Point-based value iteration: an anytime algorithm for POMDPs
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
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Solving large unknown POMDPs is an open research problem. Policy search is one solution method that is attractive as it scales in the size of the policy, which is typically much simpler than the environment. We present a global search algorithm capable of finding good policies for POMDPs that are substantially larger than previously reported results. Our algorithm is general; we show it can be used with, and improves the performance of, existing local search techniques such as gradient ascent. Sharing information between the members of the population is the key to our algorithm and we show it results in better performance than equivalent parallel searches that do not share information. Unlike previous work our algorithm does not require the size of the policy to be known in advance.