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Found 4 papers, 1 papers with code
Value-aware Importance Weighting for Off-policy Reinforcement Learning
Importance sampling is a central idea underlying off-policy prediction in reinforcement learning.
Importance Sampling Placement in Off-Policy Temporal-Difference Methods
A central challenge to applying many off-policy reinforcement learning algorithms to real world problems is the variance introduced by importance sampling.
Off-Policy Actor-Critic with Emphatic Weightings
A variety of theoretically-sound policy gradient algorithms exist for the on-policy setting due to the policy gradient theorem, which provides a simplified form for the gradient.
An Off-policy Policy Gradient Theorem Using Emphatic Weightings
There have been a host of theoretically sound algorithms proposed for the on-policy setting, due to the existence of the policy gradient theorem which provides a simplified form for the gradient.
