Sciweavers

Share
IJCAI
2001

R-MAX - A General Polynomial Time Algorithm for Near-Optimal Reinforcement Learning

8 years 9 months ago
R-MAX - A General Polynomial Time Algorithm for Near-Optimal Reinforcement Learning
R-max is a very simple model-based reinforcement learning algorithm which can attain near-optimal average reward in polynomial time. In R-max, the agent always maintains a complete, but possibly inaccurate model of its environment and acts based on the optimal policy derived from this model. The model is initialized in an optimistic fashion: all actions in all states return the maximal possible reward (hence the name). During execution, it is updated based on the agent's observations. R-max improves upon several previous algorithms: (1) It is simpler and more general than Kearns and Singh's E3 algorithm, covering zero-sum stochastic games. (2) It has a built-in mechanism for resolving the exploration vs. exploitation dilemma. (3) It formally justifies the "optimism under uncertainty" bias used in many RL algorithms. (4) It is simpler, more general, and more efficient than Brafman and Tennenholtz's LSG algorithm for learning in single controller stochastic game...
Ronen I. Brafman, Moshe Tennenholtz
Added 31 Oct 2010
Updated 31 Oct 2010
Type Conference
Year 2001
Where IJCAI
Authors Ronen I. Brafman, Moshe Tennenholtz
Comments (0)
books