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Although reinforcement learning (RL) has shown great potential in legged robot motion control, traditional methods face two major bottlenecks: the strategy training of a single robot requires a large amount of computational resources, and the trained models are highly specific and complex for direct transfer and applic...
This paper proposes a novel framework called Prior Transfer Reinforcement Learning (PTRL), which improves both training efficiency and model transferability across different robots, and introduces a fine-tuning mechanism that selectively freezes layers of the policy network during transfer.
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