Classification-based Policy Iteration with a Critic

Victor Gabillon, Alessandro Lazaric, Mohammad Ghavamzadeh, Bruno Scherrer
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
more » ... he RCPI algorithm. We present a new RCPI algorithm, called direct policy iteration with critic (DPI-Critic), and provide its finite-sample analysis when the critic is based on the LSTD method. We empirically evaluate the performance of DPI-Critic and compare it with DPI and LSPI in two benchmark reinforcement learning problems.
dblp:conf/icml/GabillonLGS11 fatcat:tosuhv4rsbax3dvr4rhz22vi5a