Correcting discount-factor mismatch in on-policy policy gradient methods
This solves a technical issue in reinforcement learning for researchers and practitioners, offering a plug-in solution to enhance existing methods, though it is incremental as it builds on prior corrections.
The paper addresses the discount-factor mismatch in on-policy policy gradient methods, which can cause degenerate learning, by introducing a novel distribution correction that improves performance and state emphasis, leading to consistent gains on benchmarks like OpenAI Gym and DeepMind suite.
The policy gradient theorem gives a convenient form of the policy gradient in terms of three factors: an action value, a gradient of the action likelihood, and a state distribution involving discounting called the \emph{discounted stationary distribution}. But commonly used on-policy methods based on the policy gradient theorem ignores the discount factor in the state distribution, which is technically incorrect and may even cause degenerate learning behavior in some environments. An existing solution corrects this discrepancy by using $γ^t$ as a factor in the gradient estimate. However, this solution is not widely adopted and does not work well in tasks where the later states are similar to earlier states. We introduce a novel distribution correction to account for the discounted stationary distribution that can be plugged into many existing gradient estimators. Our correction circumvents the performance degradation associated with the $γ^t$ correction with a lower variance. Importantly, compared to the uncorrected estimators, our algorithm provides improved state emphasis to evade suboptimal policies in certain environments and consistently matches or exceeds the original performance on several OpenAI gym and DeepMind suite benchmarks.