Improved Context-Based Offline Meta-RL with Attention and Contrastive Learning

• URL: http://arxiv.org/abs/2102.10774v1
• Date: Mon, 22 Feb 2021 05:05:16 GMT
• Title: Improved Context-Based Offline Meta-RL with Attention and Contrastive Learning
• Authors: Lanqing Li, Yuanhao Huang, Dijun Luo
• Abstract summary: We improve upon one of the SOTA OMRL algorithms, FOCAL, by incorporating intra-task attention mechanism and inter-task contrastive learning objectives. Theoretical analysis and experiments are presented to demonstrate the superior performance, efficiency and robustness of our end-to-end and model free method.
• Score: 1.3106063755117399
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Meta-learning for offline reinforcement learning (OMRL) is an understudied problem with tremendous potential impact by enabling RL algorithms in many real-world applications. A popular solution to the problem is to infer task identity as augmented state using a context-based encoder, for which efficient learning of task representations remains an open challenge. In this work, we improve upon one of the SOTA OMRL algorithms, FOCAL, by incorporating intra-task attention mechanism and inter-task contrastive learning objectives for more effective task inference and learning of control. Theoretical analysis and experiments are presented to demonstrate the superior performance, efficiency and robustness of our end-to-end and model free method compared to prior algorithms across multiple meta-RL benchmarks.

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