Technical Note: \cal Q-Learning
Machine Learning
Asynchronous Stochastic Approximation and Q-Learning
Machine Learning
Elevator Group Control Using Multiple Reinforcement Learning Agents
Machine Learning
Stochastic dynamic programming with factored representations
Artificial Intelligence
Reinforcement Learning
Recent Advances in Hierarchical Reinforcement Learning
Discrete Event Dynamic Systems
Scaling Reinforcement Learning toward RoboCup Soccer
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
Symmetry in Markov Decision Processes and its Implications for Single Agent and Multiagent Learning
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
Efficient Reinforcement Learning in Factored MDPs
IJCAI '99 Proceedings of the Sixteenth International Joint Conference on Artificial Intelligence
Reinforcement learning for factored markov decision processes
Reinforcement learning for factored markov decision processes
An algebraic approach to abstraction in reinforcement learning
An algebraic approach to abstraction in reinforcement learning
Reinforcement learning: a survey
Journal of Artificial Intelligence Research
SMDP homomorphisms: an algebraic approach to abstraction in semi-Markov decision processes
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
Generating hierarchical structure in reinforcement learning from state variables
PRICAI'00 Proceedings of the 6th Pacific Rim international conference on Artificial intelligence
Automatic abstraction in reinforcement learning using data mining techniques
Robotics and Autonomous Systems
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A reinforcement learning method is proposed that can utilize parts of states and their partial symmetries to solve a problem efficiently. In most cases the action selection does not need considering all the states but only needs looking at parts of states or restricted state of corresponding action. Moreover, restricted states of different actions are symmetrical, and thus the action value function based on restricted states can be shared which further reduces the reinforcement learning problem size. The method is compared, in terms of simulation results and other aspects, with other standard reinforcement learning methods.