The Optimal Reward Baseline for Gradient-Based Reinforcement Learning
This addresses a practical bottleneck in reinforcement learning for researchers and practitioners by reducing variance in policy gradients, though it is incremental as it builds on existing gradient-based approaches.
The paper tackles the high variance problem in gradient-based reinforcement learning by incorporating a reward baseline, showing that the optimal constant baseline equals the long-term average expected reward to minimize variance without bias. Experiments demonstrate improved performance over previous methods.
There exist a number of reinforcement learning algorithms which learnby climbing the gradient of expected reward. Their long-runconvergence has been proved, even in partially observableenvironments with non-deterministic actions, and without the need fora system model. However, the variance of the gradient estimator hasbeen found to be a significant practical problem. Recent approacheshave discounted future rewards, introducing a bias-variance trade-offinto the gradient estimate. We incorporate a reward baseline into thelearning system, and show that it affects variance without introducingfurther bias. In particular, as we approach the zero-bias,high-variance parameterization, the optimal (or variance minimizing)constant reward baseline is equal to the long-term average expectedreward. Modified policy-gradient algorithms are presented, and anumber of experiments demonstrate their improvement over previous work.