Efficiently Initializing Reinforcement Learning With Prior Policies

Posted by Ikechukwu Uchendu, AI Resident and Ted Xiao, Software Engineer, Robotics at Google Reinforcement learning (RL) can be used to train a policy to perform a task via trial and error, but a major challenge in RL is learning policies from scratch in environments with hard exploration challenges. For example, consider the setting depicted in the door-binary-v0 environment from the adroit manipulation suite, where an RL agent must control a hand in 3D space to open a door placed in front of i...

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