Reinforcement learning agents solve complex tasks without access to the reward function by training a reward predictor from human comparisons of trajectory segments, requiring feedback on less than 1% of interactions.
Introducing machine learning within an interactive evolutionary design environment
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Deep reinforcement learning from human preferences
Reinforcement learning agents solve complex tasks without access to the reward function by training a reward predictor from human comparisons of trajectory segments, requiring feedback on less than 1% of interactions.