Develops Way Off-Policy batch RL algorithms with pre-trained model priors, KL-control, and dropout uncertainty estimates to learn implicit rewards from offline human dialog data, reporting live deployment gains over prior offline methods.
On stochastic optimal control and reinforcement learning by approximate inference
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Way Off-Policy Batch Deep Reinforcement Learning of Implicit Human Preferences in Dialog
Develops Way Off-Policy batch RL algorithms with pre-trained model priors, KL-control, and dropout uncertainty estimates to learn implicit rewards from offline human dialog data, reporting live deployment gains over prior offline methods.