An Alternate Policy Gradient Estimator for Softmax Policies
This addresses a specific bottleneck in reinforcement learning for improving sample efficiency and adaptability, though it is incremental as it builds on existing policy gradient methods.
The paper tackled the problem of policy gradient estimators being ineffective with softmax policies that become sub-optimally saturated, proposing a novel estimator that uses bias and noise to escape saturation, resulting in significantly improved robustness in experiments on bandits and reinforcement learning environments.
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.