Rates of Convergence of Performance Gradient Estimates Using Function Approximation and Bias in Reinforcement Learning

Part of Advances in Neural Information Processing Systems 14 (NIPS 2001)

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Gregory Grudic, Lyle Ungar



We address two open theoretical questions in Policy Gradient Reinforce- ment Learning. The first concerns the efficacy of using function approx- imation to represent the state action value function, . Theory is pre- sented showing that linear function approximation representations of can degrade the rate of convergence of performance gradient estimates by a factor of relative to when no function approximation of is used, where is the number of basis functions in the function approximation representation. The sec- ond concerns the use of a bias term in estimating the state action value function. Theory is presented showing that a non-zero bias term can improve the rate of convergence of performance gradient estimates by is the number of possible actions. Experimen- 

  tal evidence is presented showing that these theoretical results lead to significant improvement in the convergence properties of Policy Gradi- ent Reinforcement Learning algorithms.

is the number of possible actions and