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Found 3 papers, 1 papers with code
Towards Provable Log Density Policy Gradient
In this work, we argue that this residual term is significant and correcting for it could potentially improve sample-complexity of reinforcement learning methods.
Marginalized Importance Sampling for Off-Environment Policy Evaluation
This paper proposes a new approach to evaluate the real-world performance of agent policies prior to deploying them in the real world.
Off Environment Evaluation Using Convex Risk Minimization
We also show that the our method is able to estimate performance of a 7 DOF robotic arm using the simulator and remotely collected data from the robot in the real world.
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