Elements of information theory
Elements of information theory
AAAI '98/IAAI '98 Proceedings of the fifteenth national/tenth conference on Artificial intelligence/Innovative applications of artificial intelligence
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
Reinforcement learning: a survey
Journal of Artificial Intelligence Research
The role of the asymptotic equipartition property in noiseless source coding
IEEE Transactions on Information Theory
Approximation theory of output statistics
IEEE Transactions on Information Theory
A new criterion using information gain for action selection strategy in reinforcement learning
IEEE Transactions on Neural Networks
A statistical property of multiagent learning based on Markov decision process
IEEE Transactions on Neural Networks
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Stochastic decision processes in reinforcement learning are usually formulated as Markov decision processes which are stationary and ergodic. However, in fact, some of the stochastic decision processes are not necessarily Markov, stationary, and/or ergodic. In this paper, using an information-theoretic property, we show a class of stochastic decision processes in reinforcement learning in which return maximization occurs with a positive probability. The class would be useful in considering reinforcement learning applications.