Proceedings of the seventh international conference (1990) on Machine learning
Learning probabilistic automata with variable memory length
COLT '94 Proceedings of the seventh annual conference on Computational learning theory
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
Knowledge Growth in an Artificial Animal
Proceedings of the 1st International Conference on Genetic Algorithms
Dynamic Programming
Efficient Exploration In Reinforcement Learning
Efficient Exploration In Reinforcement Learning
Greedy Utile Suffix Memory for Reinforcement Learning with Perceptually-Aliased States
Greedy Utile Suffix Memory for Reinforcement Learning with Perceptually-Aliased States
Reinforcement learning: a survey
Journal of Artificial Intelligence Research
Modeling adaptive autonomous agents
Artificial Life
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Reinforcement learning addresses the question of how an autonomous agent can learn to choose optimal actions to achieve its goals. Efficient exploration is of fundamental importance for autonomous agents that learn to act. Previous approaches to exploration in reinforcement learning usually address exploration in the case when the environment is fully observable. In contrast, we study the case when the environment is only partially observable. We consider different exploration techniques applied to the learning algorithm “Utile Suffix Memory”, and, in addition, discuss an adaptive fringe depth. Experimental results in a partially observable maze show that exploration techniques have serious impact on performance of learning algorithm.