Learning Goal-Conditioned Policies from Sub-Optimal Offline Data via Metric Learning

• URL: http://arxiv.org/abs/2402.10820v2
• Date: Sat, 8 Jun 2024 14:56:23 GMT
• Title: Learning Goal-Conditioned Policies from Sub-Optimal Offline Data via Metric Learning
• Authors: Alfredo Reichlin, Miguel Vasco, Hang Yin, Danica Kragic,
• Abstract summary: We address the problem of learning optimal behavior from sub-optimal datasets for goal-conditioned offline reinforcement learning. We propose the use of metric learning to approximate the optimal value function for goal-conditioned offline RL problems. We show that our method estimates optimal behaviors from severely sub-optimal offline datasets without suffering from out-of-distribution estimation errors.
• Score: 22.174803826742963
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We address the problem of learning optimal behavior from sub-optimal datasets for goal-conditioned offline reinforcement learning. To do so, we propose the use of metric learning to approximate the optimal value function for goal-conditioned offline RL problems under sparse rewards, invertible actions and deterministic transitions. We introduce distance monotonicity, a property for representations to recover optimality and propose an optimization objective that leads to such property. We use the proposed value function to guide the learning of a policy in an actor-critic fashion, a method we name MetricRL. Experimentally, we show that our method estimates optimal behaviors from severely sub-optimal offline datasets without suffering from out-of-distribution estimation errors. We demonstrate that MetricRL consistently outperforms prior state-of-the-art goal-conditioned RL methods in learning optimal policies from sub-optimal offline datasets.

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