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.
Deep exploration via bootstrapped dqn
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
fields
cs.LG 2years
2019 2verdicts
UNVERDICTED 2representative citing papers
Introduces a framework that learns an uncertainty-aware dynamics model and optimizes the policy via automatic differentiation through the model, reporting competitive asymptotic performance with significantly lower sample complexity than baselines on continuous control benchmarks.
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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.
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Uncertainty-aware Model-based Policy Optimization
Introduces a framework that learns an uncertainty-aware dynamics model and optimizes the policy via automatic differentiation through the model, reporting competitive asymptotic performance with significantly lower sample complexity than baselines on continuous control benchmarks.