Finite-sample convergence rates for Q-learning and indirect algorithms
Proceedings of the 1998 conference on Advances in neural information processing systems II
Markov Decision Processes: Discrete Stochastic Dynamic Programming
Markov Decision Processes: Discrete Stochastic Dynamic Programming
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
Neuro-Dynamic Programming
Near-Optimal Reinforcement Learning in Polynomial Time
Machine Learning
An Adaptive Sampling Algorithm for Solving Markov Decision Processes
Operations Research
A Fictitious Play Approach to Large-Scale Optimization
Operations Research
An intrinsic reward mechanism for efficient exploration
ICML '06 Proceedings of the 23rd international conference on Machine learning
Simulation-based Algorithms for Markov Decision Processes (Communications and Control Engineering)
Simulation-based Algorithms for Markov Decision Processes (Communications and Control Engineering)
Approximate Dynamic Programming: Solving the Curses of Dimensionality (Wiley Series in Probability and Statistics)
Sampled fictitious play for approximate dynamic programming
Computers and Operations Research
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Using Sampled Fictitious Play (SFP) concepts, we develop SFPL: Sampled Fictitious Play Learning --- a learning algorithm for solving discounted homogeneous Markov Decision Problems where the transition probabilities are unknown and need to be learned via simulation or direct observation of the system in real time. Thus, SFPL simultaneously updates the estimates of the unknown transition probabilities and the estimates of optimal value and optimal action in the observed state. In the spirit of SFP, the action after each transition is selected by sampling from the empirical distribution of previous optimal action estimates for the current state. The resulting algorithm is provably convergent. We compare its performance with other learning methods, including SARSA and Q-learning.