Paper-Conference

Switching the Loss Reduces the Cost in Batch Reinforcement Learning

When is Offline Policy Selection Feasible for Reinforcement Learning?

Asymptotically Unbiased Off-Policy Policy Evaluation when Reusing Old Data in Nonstationary Environments

Measuring and Mitigating Interference in Reinforcement Learning

Training Recurrent Neural Networks Online by Learning Explicit State Variables

The utility of sparse representations for control in reinforcement learning

A Value Function Basis for Nexting and Multi-step Prediction

Incrementally Learning Functions of the Return