Reinforcement Learning
Co-AdaptationofAlgorithmicand ImplementationalInnovationsin Inference-basedDeepReinforcementLearning
Recently many algorithms were devised for reinforcement learning (RL) with functionapproximation.
Experiment Planning with Function Approximation
We study the problem of experiment planning with function approximation in contextual bandit problems.
Non-AsymptoticAnalysisforTwoTime-scaleTDC withGeneralSmoothFunctionApproximation
Temporaldifference(TD)learning algorithm is one of the most popular policy evaluation approaches.
Percentile Criterion Optimization in Offline Reinforcement Learning
In reinforcement learning, robust policies for high-stakes decision-making problems with limited data are usually computed by optimizing the percentile criterion .
