Stochastic systems: estimation, identification and adaptive control
Stochastic systems: estimation, identification and adaptive control
Artificial intelligence: a modern approach
Artificial intelligence: a modern approach
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
Self-Optimizing and Pareto-Optimal Policies in General Environments Based on Bayes-Mixtures
COLT '02 Proceedings of the 15th Annual Conference on Computational Learning Theory
Optimality of universal Bayesian sequence prediction for general loss and alphabet
The Journal of Machine Learning Research
Universal Artificial Intelligence: Sequential Decisions Based On Algorithmic Probability
Universal Artificial Intelligence: Sequential Decisions Based On Algorithmic Probability
Prediction, Learning, and Games
Prediction, Learning, and Games
Information theory and statistics: a tutorial
Communications and Information Theory
R-MAX: a general polynomial time algorithm for near-optimal reinforcement learning
IJCAI'01 Proceedings of the 17th international joint conference on Artificial intelligence - Volume 2
Reinforcement learning in POMDPs without resets
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
Defensive universal learning with experts
ALT'05 Proceedings of the 16th international conference on Algorithmic Learning Theory
Efficient Reinforcement Learning in Parameterized Models: Discrete Parameter Case
Recent Advances in Reinforcement Learning
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We address the problem of reinforcement learning in which observations may exhibit an arbitrary form of stochastic dependence on past observations and actions, i.e. environments more general than (PO) MDPs. The task for an agent is to attain the best possible asymptotic reward where the true generating environment is unknown but belongs to a known countable family of environments. We find some sufficient conditions on the class of environments under which an agent exists which attains the best asymptotic reward for any environment in the class. We analyze how tight these conditions are and how they relate to different probabilistic assumptions known in reinforcement learning and related fields, such as Markov Decision Processes and mixing conditions.