HMRL: Hyper-Meta Learning for Sparse Reward Reinforcement Learning Problem

• URL: http://arxiv.org/abs/2002.04238v2
• Date: Sat, 5 Jun 2021 06:36:21 GMT
• Title: HMRL: Hyper-Meta Learning for Sparse Reward Reinforcement Learning Problem
• Authors: Yun Hua, Xiangfeng Wang, Bo Jin, Wenhao Li, Junchi Yan, Xiaofeng He, Hongyuan Zha
• Abstract summary: We develop a novel meta reinforcement learning framework called Hyper-Meta RL(HMRL) for sparse reward RL problems. It is consisted with three modules including the cross-environment meta state embedding module which constructs a common meta state space to adapt to different environments. Experiments with sparse-reward environments show the superiority of HMRL on both transferability and policy learning efficiency.
• Score: 107.52043871875898
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In spite of the success of existing meta reinforcement learning methods, they still have difficulty in learning a meta policy effectively for RL problems with sparse reward. In this respect, we develop a novel meta reinforcement learning framework called Hyper-Meta RL(HMRL), for sparse reward RL problems. It is consisted with three modules including the cross-environment meta state embedding module which constructs a common meta state space to adapt to different environments; the meta state based environment-specific meta reward shaping which effectively extends the original sparse reward trajectory by cross-environmental knowledge complementarity and as a consequence the meta policy achieves better generalization and efficiency with the shaped meta reward. Experiments with sparse-reward environments show the superiority of HMRL on both transferability and policy learning efficiency.

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