Goal-Conditioned Data Augmentation for Offline Reinforcement Learning

• URL: http://arxiv.org/abs/2412.20519v1
• Date: Sun, 29 Dec 2024 16:42:30 GMT
• Title: Goal-Conditioned Data Augmentation for Offline Reinforcement Learning
• Authors: Xingshuai Huang, Di Wu Member, Benoit Boulet,
• Abstract summary: We introduce Goal-cOnditioned Data Augmentation (GODA), a goal-conditioned diffusion-based method for augmenting samples with higher quality. GODA learns a comprehensive distribution representation of the original offline datasets while generating new data with selectively higher-return goals. We conduct experiments on the D4RL benchmark and real-world challenges, specifically traffic signal control (TSC) tasks, to demonstrate GODA's effectiveness.
• Score: 3.5775697416994485
• License:
• Abstract: Offline reinforcement learning (RL) enables policy learning from pre-collected offline datasets, relaxing the need to interact directly with the environment. However, limited by the quality of offline datasets, it generally fails to learn well-qualified policies in suboptimal datasets. To address datasets with insufficient optimal demonstrations, we introduce Goal-cOnditioned Data Augmentation (GODA), a novel goal-conditioned diffusion-based method for augmenting samples with higher quality. Leveraging recent advancements in generative modeling, GODA incorporates a novel return-oriented goal condition with various selection mechanisms. Specifically, we introduce a controllable scaling technique to provide enhanced return-based guidance during data sampling. GODA learns a comprehensive distribution representation of the original offline datasets while generating new data with selectively higher-return goals, thereby maximizing the utility of limited optimal demonstrations. Furthermore, we propose a novel adaptive gated conditioning method for processing noised inputs and conditions, enhancing the capture of goal-oriented guidance. We conduct experiments on the D4RL benchmark and real-world challenges, specifically traffic signal control (TSC) tasks, to demonstrate GODA's effectiveness in enhancing data quality and superior performance compared to state-of-the-art data augmentation methods across various offline RL algorithms.

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