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.
Td algorithm for the variance of return and mean-variance reinforcement learning
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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.