Technical Note: \cal Q-Learning
Machine Learning
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
Near-Optimal Reinforcement Learning in Polynomial Time
Machine Learning
Dynamic Programming
Finite-memory control of partially observable systems
Finite-memory control of partially observable systems
R-max - a general polynomial time algorithm for near-optimal reinforcement learning
The Journal of Machine Learning Research
Prioritization Methods for Accelerating MDP Solvers
The Journal of Machine Learning Research
A theoretical analysis of Model-Based Interval Estimation
ICML '05 Proceedings of the 22nd international conference on Machine learning
A dynamic topological sort algorithm for directed acyclic graphs
Journal of Experimental Algorithmics (JEA)
Topological value iteration algorithm for Markov decision processes
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
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The goal of Reinforcement learning (RL) is to maximize reward (minimize cost) in a Markov decision process (MDP) without knowing the underlying model a priori. RL algorithms tend to be much slower than planning algorithms, which require the model as input. Recent results demonstrate that MDP planning can be expedited, by exploiting the graphical structure of the MDP. We present extensions to two popular RL algorithms, Q-learning and RMax, that learn and exploit the graphical structure of problems to improve overall learning speed. Use of the graphical structure of the underlying MDP can greatly improve the speed of planning algorithms, if the underlying MDP has a nontrivial topological structure. Our experiments show that use of the apparent topological structure of an MDP speeds up reinforcement learning, even if the MDP is simply connected.