Deterministic Policy Optimization by Combining Pathwise and Score Function Estimators for Discrete Action Spaces
This work addresses the problem of sample efficiency in reinforcement learning for discrete action spaces, offering a method that bridges a gap in existing hybrid approaches, though it is incremental in extending techniques to a new domain.
The paper tackles the challenge of applying hybrid policy optimization methods to discrete action spaces, which were previously limited to continuous ones, by introducing a novel hybrid policy gradient estimator that combines score function and pathwise derivative estimators. The result shows significant gains in sample complexity, with improvements ranging from 1.7x to 25x on benchmark tasks like Cart Pole and Acrobot.
Policy optimization methods have shown great promise in solving complex reinforcement and imitation learning tasks. While model-free methods are broadly applicable, they often require many samples to optimize complex policies. Model-based methods greatly improve sample-efficiency but at the cost of poor generalization, requiring a carefully handcrafted model of the system dynamics for each task. Recently, hybrid methods have been successful in trading off applicability for improved sample-complexity. However, these have been limited to continuous action spaces. In this work, we present a new hybrid method based on an approximation of the dynamics as an expectation over the next state under the current policy. This relaxation allows us to derive a novel hybrid policy gradient estimator, combining score function and pathwise derivative estimators, that is applicable to discrete action spaces. We show significant gains in sample complexity, ranging between $1.7$ and $25\times$, when learning parameterized policies on Cart Pole, Acrobot, Mountain Car and Hand Mass. Our method is applicable to both discrete and continuous action spaces, when competing pathwise methods are limited to the latter.