Practical numerical algorithms for chaotic systems
Practical numerical algorithms for chaotic systems
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
A framework for aggregation of multiple reinforcement learning algorithms
A framework for aggregation of multiple reinforcement learning algorithms
Quantum Chaotic Reinforcement Learning
ICNC '08 Proceedings of the 2008 Fourth International Conference on Natural Computation - Volume 03
Multiagent Systems: Algorithmic, Game-Theoretic, and Logical Foundations
Multiagent Systems: Algorithmic, Game-Theoretic, and Logical Foundations
Evolutionary Multi-objective Optimization in Uncertain Environments: Issues and Algorithms
Evolutionary Multi-objective Optimization in Uncertain Environments: Issues and Algorithms
Improving reinforcement learning agents using genetic algorithms
AMT'10 Proceedings of the 6th international conference on Active media technology
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In reinforcement learning exploration phase, it is necessary to introduce a process of trial and error to discover better rewards obtained from environment. To this end, one usually uses the uniform pseudorandom number generator in exploration phase. However, it is known that chaotic source also provides a random-like sequence similar to stochastic source. In this paper we have employed the chaotic generator in the exploration phase of reinforcement learning in a nondeterministic maze problem. We obtained promising results in the so called maze problem.