Task-Aware Virtual Training: Enhancing Generalization in Meta-Reinforcement Learning for Out-of-Distribution Tasks

• URL: http://arxiv.org/abs/2502.02834v2
• Date: Fri, 14 Feb 2025 11:19:06 GMT
• Title: Task-Aware Virtual Training: Enhancing Generalization in Meta-Reinforcement Learning for Out-of-Distribution Tasks
• Authors: Jeongmo Kim, Yisak Park, Minung Kim, Seungyul Han,
• Abstract summary: Task-Aware Virtual Training (TAVT) is a novel algorithm that captures task characteristics for both training and out-of-distribution (OOD) scenarios. Numerical results demonstrate that TAVT significantly enhances generalization to OOD tasks across various MuJoCo and MetaWorld environments.
• Score: 4.374837991804085
• License:
• Abstract: Meta reinforcement learning aims to develop policies that generalize to unseen tasks sampled from a task distribution. While context-based meta-RL methods improve task representation using task latents, they often struggle with out-of-distribution (OOD) tasks. To address this, we propose Task-Aware Virtual Training (TAVT), a novel algorithm that accurately captures task characteristics for both training and OOD scenarios using metric-based representation learning. Our method successfully preserves task characteristics in virtual tasks and employs a state regularization technique to mitigate overestimation errors in state-varying environments. Numerical results demonstrate that TAVT significantly enhances generalization to OOD tasks across various MuJoCo and MetaWorld environments.

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