Wasserstein Policy Optimization

We introduce Wasserstein Policy Optimization (WPO), an actor-critic algorithm for reinforcement learning in continuous action spaces. WPO can be derived as an approximation to Wasserstein gradient flow over the space of all policies projected into a finite-dimensional parameter space (e. g. , the weights of a neural network), leading to a simple and completely general closed-form update. The resulting algorithm combines many properties of deterministic and classic policy gradient methods. Like deterministic policy gradients, it exploits knowledge of the gradient of the action-value function with respect to the action. Like classic policy gradients, it can be applied to stochastic policies with arbitrary distributions over actions – without using the reparameterization trick. We show results on the DeepMind Control Suite and a magnetic confinement fusion task which compare favorably with state-of-the-art continuous control methods.

Authors

• David Pfau
• Ian Davies
• Diana Borsa
• João Guilherme Madeira Araújo
• Brendan D. Tracey
• Hado van Hasselt

Keywords

• Policy Optimization
• Wasserstein metric
• Optimal Transport
• Gradient Flow
• Deep Reinforcement Learning
• Actor-Critic
• Continuous Control