Near-Optimal Reinforcement Learning in Polynominal Time
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
Universal Artificial Intelligence: Sequential Decisions Based On Algorithmic Probability
Universal Artificial Intelligence: Sequential Decisions Based On Algorithmic Probability
A theoretical analysis of Model-Based Interval Estimation
ICML '05 Proceedings of the 22nd international conference on Machine learning
On the possibility of learning in reactive environments with arbitrary dependence
Theoretical Computer Science
Reinforcement learning in POMDPs without resets
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
Artificial Intelligence: A Modern Approach
Artificial Intelligence: A Modern Approach
Optimality issues of universal greedy agents with static priors
ALT'10 Proceedings of the 21st international conference on Algorithmic learning theory
ALT'11 Proceedings of the 22nd international conference on Algorithmic learning theory
ALT'11 Proceedings of the 22nd international conference on Algorithmic learning theory
PAC bounds for discounted MDPs
ALT'12 Proceedings of the 23rd international conference on Algorithmic Learning Theory
AGI'12 Proceedings of the 5th international conference on Artificial General Intelligence
Learning agents with evolving hypothesis classes
AGI'13 Proceedings of the 6th international conference on Artificial General Intelligence
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We use optimism to introduce generic asymptotically optimal reinforcement learning agents. They achieve, with an arbitrary finite or compact class of environments, asymptotically optimal behavior. Furthermore, in the finite deterministic case we provide finite error bounds.