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Automatic Environment Shaping is the Next Frontier in RL Automatic Environment Shaping is the Next Frontier in RL Younghyo Park* Gabriel B. Margolis* Pulkit Agrawal Position Paper at ICML 2024 (Oral) Oral Session: Wed 24 Jul 10:30 AM in [Hall C 1-3] Full Paper Code Repository Twitter Abstract Many roboticists dream of presenting a robot with a task in the evening and returning the next morning to find the robot capable of solving the task. What is preventing us from achieving this? Sim-to-real reinforcement learning (RL) has achieved impressive performance on challenging robotics tasks, but requires substantial human effort to set up the task in a way that is amenable to RL. It's our position that algorithmic improvements in policy optimization and other ideas should be guided towards resolving the primary bottleneck of shaping the training environment, i.e., designing observations, actions, rewards and simulation dynamics. Most practitioners don't tune the RL...
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