Learning to Perceive and Act by Trial and Error
Machine Learning
Reinforcement learning with hidden states
Proceedings of the second international conference on From animals to animats 2 : simulation of adaptive behavior: simulation of adaptive behavior
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
Reinforcement learning with selective perception and hidden state
Reinforcement learning with selective perception and hidden state
Reinforcement learning: a survey
Journal of Artificial Intelligence Research
Model-based online learning of POMDPs
ECML'05 Proceedings of the 16th European conference on Machine Learning
Online expectation maximization for reinforcement learning in POMDPs
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
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This paper addresses the problem of constructing good action selection policies for agents acting in partially observable environments, a class of problems generally known as Partially Observable Markov Decision Processes. We present a novel approach that uses a modification of the well-known Baum-Welch algorithm for learning a Hidden Markov Model (HMM) to predict both percepts and utility in a non-deterministic world. This enables an agent to make decisions based on its previous history of actions, observations, and rewards. Our algorithm, called Utile Distinction Hidden Markov Models (UDHMM), handles the creation of memory well in that it tends to create perceptual and utility distinctions only when needed, while it can still discriminate states based on histories of arbitrary length. The experimental results in highly stochastic problem domains show very good performance.