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arXiv · 2006.07781

Non-local Policy Optimization via Diversity-regularized Collaborative Exploration

Zhenghao Peng·Hao Sun·Bolei Zhou
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Abstract

Conventional Reinforcement Learning (RL) algorithms usually have one single agent learning to solve the task independently. As a result, the agent can only explore a limited part of the state-action space while the learned behavior is highly correlated to the agent's previous experience, making the training prone to a local minimum. In this work, we empower RL with the capability of teamwork and propose a novel non-local policy optimization framework called Diversity-regularized Collaborative Exploration (DiCE). DiCE utilizes a group of heterogeneous agents to explore the environment simultaneously and share the collected experiences. A regularization mechanism is further designed to maintain the diversity of the team and modulate the exploration. We implement the framework in both on-policy and off-policy settings and the experimental results show that DiCE can achieve substantial improvement over the baselines in the MuJoCo locomotion tasks.

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Zhenghao Peng, Hao Sun, Bolei Zhou. 2020-06-14. Non-local Policy Optimization via Diversity-regularized Collaborative Exploration. https://arxiv.org/abs/2006.07781

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