Proceedings of the seventh international conference (1990) on Machine learning
Empirical methods for artificial intelligence
Empirical methods for artificial intelligence
Efficient model-based exploration
Proceedings of the fifth international conference on simulation of adaptive behavior on From animals to animats 5
Analytical Mean Squared Error Curves for Temporal DifferenceLearning
Machine Learning
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
Neuro-Dynamic Programming
Learning to Predict by the Methods of Temporal Differences
Machine Learning
Artificial Intelligence: A Modern Approach
Artificial Intelligence: A Modern Approach
Efficient Exploration In Reinforcement Learning
Efficient Exploration In Reinforcement Learning
R-max - a general polynomial time algorithm for near-optimal reinforcement learning
The Journal of Machine Learning Research
Qualitative reinforcement learning
ICML '06 Proceedings of the 23rd international conference on Machine learning
The many faces of optimism: a unifying approach
Proceedings of the 25th international conference on Machine learning
Reinforcement learning: a survey
Journal of Artificial Intelligence Research
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The key problem in reinforcement learning is the explorationexploitation tradeoff. An optimistic initialisation of the value function is a popular RL strategy. The problem of this approach is that the algorithm may have relatively low performance after many episodes of learning. In this paper, two extensions to standard optimistic exploration are proposed. The first one is based on different initialisation of the value function of goal states. The second one which builds on the previous idea explicitly separates propagation of low and high values in the state space. Proposed extensions show improvement in empirical comparisons with basic optimistic initialisation. Additionally, they improve anytime performance and help on domains where learning takes place on the subspace of the large state space, that is, where the standard optimistic approach faces more difficulties.