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This document presents a novel pretraining-finetuning approach for reinforcement learning in robot locomotion that enhances sample efficiency and task performance. By utilizing an exploration-based data collection strategy to train a Proprioceptive Inverse Dynamics Model (PIDM), the authors demonstrate significant improvements in learning efficiency when initializing actor-critic networks with pretrained weights. The proposed method shows an average improvement of 36.9% in sample efficiency and 7.3% in task

Author

itachi.physics

Language

EN