Abstract
Data detailed on robotics have been presented. According to news reporting out of Shanghai, People's Republic of China, by NewsRx editors, research stated, “The ability to learn continuously is crucial for a robot to achieve a high level of intelligence and autonomy.” Funders for this research include National Science And Technology Innovation 2030; Nsfc General Program. The news editors obtained a quote from the research from Fudan University: “In this paper, we consider continual reinforcement learning (RL) for quadruped robots, which includes the ability to continuously learn sub-sequential tasks (plasticity) and maintain performance on previous tasks (stability). The policy obtained by the proposed method enables robots to learn multiple tasks sequentially, while overcoming both catastrophic forgetting and loss of plasticity. At the same time, it achieves the above goals with as little modification to the original RL learning process as possible. The proposed method uses the Piggyback algorithm to select protected parameters for each task, and reinitializes the unused parameters to increase plasticity. Meanwhile, we encourage the policy network exploring by encouraging the entropy of the soft network of the policy network.”
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