Minimax PAC bounds on the sample complexity of reinforcement learning with a generative model

• Published in: Machine learning Vol. 91; no. 3; pp. 325 - 349
• Main Authors: Gheshlaghi Azar, Mohammad, Munos, Rémi, Kappen, Hilbert J.
• Format: Journal Article
• Language: English
• Published: Boston Springer US 01.06.2013; Springer; Springer Nature B.V; Springer Verlag
• Subjects: Applied sciences; Artificial Intelligence; Complexity; Computer Science; Computer science; control theory; systems; Control; Decision making models; Exact sciences and technology; Learning; Learning and adaptive systems; Lower bounds; Machine Learning; Mathematical models; Mechatronics; Minimax technique; Natural Language Processing (NLP); Optimization; Policies; Reinforcement; Robotics; Simulation and Modeling; Sample complexity; Learning theory; Markov decision processes; Reinforcement learning; Markov process; Lower bound; Markov decision; Probabilistic approach; Optimal policy; Optimal estimation; Modeling; Upper bound; Value function; Minimax problem; Transition state; Minimax method; Reward; Artificial intelligence; Generative model

We consider the problems of learning the optimal action-value function and the optimal policy in discounted-reward Markov decision processes (MDPs). We prove new PAC bounds on the sample-complexity of two well-known model-based reinforcement learning (RL) algorithms in the presence of a generative m...