Controlling the movements of highly articulated agents and robots has been a long-standing challenge to model-free deep reinforcement learning. In this paper, we propose a simple, yet general, framework for improving the performance of policy gradient algorithms by discretizing the continuous action space. Instead of using a fixed set of predetermined atomic actions, we exploit particle filtering to adaptively discretize actions during training and track the posterior policy distribution represented as a mixture of Gaussians. The resulting policy can replace the original continuous policy of any given policy gradient algorithm without changing its underlying model architecture. We demonstrate the applicability of our approach to state-of-the-art on-policy and off-policy baselines in challenging control tasks. Baselines using our particle-based policies achieve better final performance and speed of convergence as compared to corresponding continuous implementations and implementations that rely on fixed discretization schemes.
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