Skill-Enhanced Reinforcement Learning Acceleration from Heterogeneous Demonstrations
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arXiv
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| Auteurs principaux: | , |
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| Format: | Preprint |
| Publié: |
2024
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| _version_ | 1866909760488472576 |
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| author | Zhang, Hanping Guo, Yuhong |
| author_facet | Zhang, Hanping Guo, Yuhong |
| contents | Learning from Demonstration (LfD) is a well-established problem in Reinforcement Learning (RL), which aims to facilitate rapid RL by leveraging expert demonstrations to pre-train the RL agent. However, the limited availability of expert demonstration data often hinders its ability to effectively aid downstream RL learning. To address this problem, we propose a novel two-stage method dubbed as Skill-enhanced Reinforcement Learning Acceleration (SeRLA). SeRLA introduces a skill-level adversarial Positive-Unlabeled (PU) learning model that extracts useful skill prior knowledge by learning from both expert demonstrations and general low-cost demonstrations in the offline prior learning stage. Building on this, it employs a skill-based soft actor-critic algorithm to leverage the acquired priors for efficient training of a skill policy network in the downstream online RL stage. In addition, we propose a simple skill-level data enhancement technique to mitigate data sparsity and further improve both skill prior learning and skill policy training. Experiments across multiple standard RL benchmarks demonstrate that SeRLA achieves state-of-the-art performance in accelerating reinforcement learning on downstream tasks, particularly in the early training phase. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2412_06207 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Skill-Enhanced Reinforcement Learning Acceleration from Heterogeneous Demonstrations Zhang, Hanping Guo, Yuhong Machine Learning Artificial Intelligence Learning from Demonstration (LfD) is a well-established problem in Reinforcement Learning (RL), which aims to facilitate rapid RL by leveraging expert demonstrations to pre-train the RL agent. However, the limited availability of expert demonstration data often hinders its ability to effectively aid downstream RL learning. To address this problem, we propose a novel two-stage method dubbed as Skill-enhanced Reinforcement Learning Acceleration (SeRLA). SeRLA introduces a skill-level adversarial Positive-Unlabeled (PU) learning model that extracts useful skill prior knowledge by learning from both expert demonstrations and general low-cost demonstrations in the offline prior learning stage. Building on this, it employs a skill-based soft actor-critic algorithm to leverage the acquired priors for efficient training of a skill policy network in the downstream online RL stage. In addition, we propose a simple skill-level data enhancement technique to mitigate data sparsity and further improve both skill prior learning and skill policy training. Experiments across multiple standard RL benchmarks demonstrate that SeRLA achieves state-of-the-art performance in accelerating reinforcement learning on downstream tasks, particularly in the early training phase. |
| title | Skill-Enhanced Reinforcement Learning Acceleration from Heterogeneous Demonstrations |
| topic | Machine Learning Artificial Intelligence |
| url | https://arxiv.org/abs/2412.06207 |