An Alternate Policy Gradient Estimator for Softmax Policies

TL;DR

This paper introduces a new policy gradient estimator for softmax policies that leverages bias and noise to improve robustness against policy saturation, enhancing sample efficiency and adaptability.

Contribution

A novel policy gradient estimator that effectively escapes saturated policies by exploiting critic bias and reward noise, improving learning efficiency.

Findings

More robust to policy saturation in experiments

Requires fewer updates to overcome sub-optimal policies

Effective in bandits and reinforcement learning environments

Abstract

Policy gradient (PG) estimators are ineffective in dealing with softmax policies that are sub-optimally saturated, which refers to the situation when the policy concentrates its probability mass on sub-optimal actions. Sub-optimal policy saturation may arise from bad policy initialization or sudden changes in the environment that occur after the policy has already converged. Current softmax PG estimators require a large number of updates to overcome policy saturation, which causes low sample efficiency and poor adaptability to new situations. To mitigate this problem, we propose a novel PG estimator for softmax policies that utilizes the bias in the critic estimate and the noise present in the reward signal to escape the saturated regions of the policy parameter space. Our theoretical analysis and experiments, conducted on bandits and various reinforcement learning environments, show that this new estimator is significantly more robust to policy saturation.