Search Results for author:
Found 5 papers, 0 papers with code
Future Prediction Can be a Strong Evidence of Good History Representation in Partially Observable Environments
Then, our main contributions are two-fold: (a) we demonstrate that the performance of reinforcement learning is strongly correlated with the prediction accuracy of future observations in partially observable environments, and (b) our approach can significantly improve the overall end-to-end approach by preventing high-variance noisy signals from reinforcement learning objectives to influence the representation learning.
SPEED: Experimental Design for Policy Evaluation in Linear Heteroscedastic Bandits
In this paper, we study the problem of optimal data collection for policy evaluation in linear bandits.
Reducing Sampling Error in Batch Temporal Difference Learning
In this batch setting, we show that TD(0) may converge to an inaccurate value function because the update following an action is weighted according to the number of times that action occurred in the batch -- not the true probability of the action under the given policy.
An Imitation from Observation Approach to Transfer Learning with Dynamics Mismatch
We examine the problem of transferring a policy learned in a source environment to a target environment with different dynamics, particularly in the case where it is critical to reduce the amount of interaction with the target environment during learning.
Approximation of Lorenz-Optimal Solutions in Multiobjective Markov Decision Processes
This paper is devoted to fair optimization in Multiobjective Markov Decision Processes (MOMDPs).
