GOPlan: Goal-conditioned Offline Reinforcement Learning by Planning with Learned Models

• URL: http://arxiv.org/abs/2310.20025v3
• Date: Thu, 16 May 2024 14:08:55 GMT
• Title: GOPlan: Goal-conditioned Offline Reinforcement Learning by Planning with Learned Models
• Authors: Mianchu Wang, Rui Yang, Xi Chen, Hao Sun, Meng Fang, Giovanni Montana,
• Abstract summary: Goal-conditioned Offline Planning (GOPlan) is a novel model-based framework that contains two key phases. GOPlan pretrains a prior policy capable of capturing multi-modal action distribution within the multi-goal dataset. The reanalysis method generates high-quality imaginary data by planning with learned models for both intra-trajectory and inter-trajectory goals.
• Score: 31.628341050846768
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Offline Goal-Conditioned RL (GCRL) offers a feasible paradigm for learning general-purpose policies from diverse and multi-task offline datasets. Despite notable recent progress, the predominant offline GCRL methods, mainly model-free, face constraints in handling limited data and generalizing to unseen goals. In this work, we propose Goal-conditioned Offline Planning (GOPlan), a novel model-based framework that contains two key phases: (1) pretraining a prior policy capable of capturing multi-modal action distribution within the multi-goal dataset; (2) employing the reanalysis method with planning to generate imagined trajectories for funetuning policies. Specifically, we base the prior policy on an advantage-weighted conditioned generative adversarial network, which facilitates distinct mode separation, mitigating the pitfalls of out-of-distribution (OOD) actions. For further policy optimization, the reanalysis method generates high-quality imaginary data by planning with learned models for both intra-trajectory and inter-trajectory goals. With thorough experimental evaluations, we demonstrate that GOPlan achieves state-of-the-art performance on various offline multi-goal navigation and manipulation tasks. Moreover, our results highlight the superior ability of GOPlan to handle small data budgets and generalize to OOD goals.

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