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Meta-Reinforcement Learning for Adaptive Motor Control in Changing Robot Dynamics and Environments

By Timothée Anne and others
This work developed a meta-learning approach that adapts the control policy on the fly to different changing conditions for robust locomotion. The proposed method constantly updates the interaction model, samples feasible sequences of actions of estimated the state-action trajectories, and then applies the optimal actions to maximize the reward. To... Show more
January 19, 2021
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Meta-Reinforcement Learning for Adaptive Motor Control in Changing Robot Dynamics and Environments
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