LEO enables efficient all-goals learning in goal-conditioned RL by jointly predicting for all goals in one network pass, yielding >250x speedup over relabelling and better performance on Craftax.
Advances in neural information processing systems , volume=
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Offline RL policies trained solely on DIII-D historical data were deployed on the tokamak and produced promising real-world control of the plasma rotation profile.
citing papers explorer
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Goal-Conditioned Agents that Learn Everything All at Once
LEO enables efficient all-goals learning in goal-conditioned RL by jointly predicting for all goals in one network pass, yielding >250x speedup over relabelling and better performance on Craftax.
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Offline Reinforcement Learning for Rotation Profile Control in Tokamaks
Offline RL policies trained solely on DIII-D historical data were deployed on the tokamak and produced promising real-world control of the plasma rotation profile.