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Classification-based Policy Iteration with a Critic
2011
International Conference on Machine Learning
In this paper, we study the effect of adding a value function approximation component (critic) to rollout classification-based policy iteration (RCPI) algorithms. The idea is to use a critic to approximate the return after we truncate the rollout trajectories. This allows us to control the bias and variance of the rollout estimates of the action-value function. Therefore, the introduction of a critic can improve the accuracy of the rollout estimates, and as a result, enhance the performance of
dblp:conf/icml/GabillonLGS11
fatcat:tosuhv4rsbax3dvr4rhz22vi5a