Model-Based Offline Meta-Reinforcement Learning with Regularization

• URL: http://arxiv.org/abs/2202.02929v1
• Date: Mon, 7 Feb 2022 04:15:20 GMT
• Title: Model-Based Offline Meta-Reinforcement Learning with Regularization
• Authors: Sen Lin, Jialin Wan, Tengyu Xu, Yingbin Liang, Junshan Zhang
• Abstract summary: offline Meta-RL is emerging as a promising approach to address these challenges. MerPO learns a meta-model for efficient task structure inference and an informative meta-policy. We show that MerPO offers guaranteed improvement over both the behavior policy and the meta-policy.
• Score: 63.35040401948943
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Existing offline reinforcement learning (RL) methods face a few major challenges, particularly the distributional shift between the learned policy and the behavior policy. Offline Meta-RL is emerging as a promising approach to address these challenges, aiming to learn an informative meta-policy from a collection of tasks. Nevertheless, as shown in our empirical studies, offline Meta-RL could be outperformed by offline single-task RL methods on tasks with good quality of datasets, indicating that a right balance has to be delicately calibrated between "exploring" the out-of-distribution state-actions by following the meta-policy and "exploiting" the offline dataset by staying close to the behavior policy. Motivated by such empirical analysis, we explore model-based offline Meta-RL with regularized Policy Optimization (MerPO), which learns a meta-model for efficient task structure inference and an informative meta-policy for safe exploration of out-of-distribution state-actions. In particular, we devise a new meta-Regularized model-based Actor-Critic (RAC) method for within-task policy optimization, as a key building block of MerPO, using conservative policy evaluation and regularized policy improvement; and the intrinsic tradeoff therein is achieved via striking the right balance between two regularizers, one based on the behavior policy and the other on the meta-policy. We theoretically show that the learnt policy offers guaranteed improvement over both the behavior policy and the meta-policy, thus ensuring the performance improvement on new tasks via offline Meta-RL. Experiments corroborate the superior performance of MerPO over existing offline Meta-RL methods.

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