Divergence-Augmented Policy Optimization
Qing Wang · Yingru Li · Jiechao Xiong · Tong Zhang

Wed Dec 11th 10:45 AM -- 12:45 PM @ East Exhibition Hall B + C #204

In deep reinforcement learning, policy optimization methods need to deal with issues such as function approximation and the reuse of off-policy data. Standard policy gradient methods do not handle off-policy data well, leading to premature convergence and instability. This paper introduces a method to stabilize policy optimization when off-policy data are reused. The idea is to include a Bregman divergence between the behavior policy that generates the data and the current policy to ensure small and safe policy updates with off-policy data. The Bregman divergence is calculated between the state distributions of two policies, instead of only on the action probabilities, leading to a divergence augmentation formulation. Empirical experiments on Atari games show that in the data-scarce scenario where the reuse of off-policy data becomes necessary, our method can achieve better performance than other state-of-the-art deep reinforcement learning algorithms.

Author Information

Qing Wang (Huya AI)
Richard Yingru Li (The Chinese University of Hong Kong, Shenzhen, China)
Jiechao Xiong (Tencent AI Lab)
Tong Zhang (Tencent AI Lab)

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