Non-local Policy Optimization via Diversity-regularized Collaborative Exploration

• URL: http://arxiv.org/abs/2006.07781v1
• Date: Sun, 14 Jun 2020 03:31:11 GMT
• Title: Non-local Policy Optimization via Diversity-regularized Collaborative Exploration
• Authors: Zhenghao Peng, Hao Sun, Bolei Zhou
• Abstract summary: We 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. 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.
• Score: 45.997521480637836
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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