POPO: Pessimistic Offline Policy Optimization

• URL: http://arxiv.org/abs/2012.13682v2
• Date: Mon, 4 Jan 2021 03:43:14 GMT
• Title: POPO: Pessimistic Offline Policy Optimization
• Authors: Qiang He, Xinwen Hou
• Abstract summary: We study why off-policy RL methods fail to learn in offline setting from the value function view. We propose Pessimistic Offline Policy Optimization (POPO), which learns a pessimistic value function to get a strong policy. We find that POPO performs surprisingly well and scales to tasks with high-dimensional state and action space.
• Score: 6.122342691982727
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Offline reinforcement learning (RL), also known as batch RL, aims to optimize policy from a large pre-recorded dataset without interaction with the environment. This setting offers the promise of utilizing diverse, pre-collected datasets to obtain policies without costly, risky, active exploration. However, commonly used off-policy algorithms based on Q-learning or actor-critic perform poorly when learning from a static dataset. In this work, we study why off-policy RL methods fail to learn in offline setting from the value function view, and we propose a novel offline RL algorithm that we call Pessimistic Offline Policy Optimization (POPO), which learns a pessimistic value function to get a strong policy. We find that POPO performs surprisingly well and scales to tasks with high-dimensional state and action space, comparing or outperforming several state-of-the-art offline RL algorithms on benchmark tasks.

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