markov decision process

Optimistic posterior sampling for reinforcement learning: worst-case regret bounds NeurIPS 2017 POMDPs.jl: A Framework for Sequential Decision Making under Uncertainty JMLR 2017 Reduction Techniques for Model Checking and Learning in MDPs IJCAI 2017 Regret Minimization in MDPs with Options without Prior Knowledge NeurIPS 2017 Reinforcement Learning with a Corrupted Reward Channel IJCAI 2017 Runtime Neural Pruning NeurIPS 2017 Unifying PAC and Regret: Uniform PAC Bounds for Episodic Reinforcement Learning NeurIPS 2017 Unifying Task Specification in Reinforcement Learning ICML 2017 Why is Posterior Sampling Better than Optimism for Reinforcement Learning? ICML 2017 Addressing Environment Non-Stationarity by Repeating Q-learning Updates JMLR 2016 A Fast and Reliable Policy Improvement Algorithm AISTATS 2016 Approximate Newton Methods for Policy Search in Markov Decision Processes JMLR 2016 A Stochastic Model for Computer-Aided Human-Human Dialogue Interspeech 2016 Bayesian Decision Process for Cost-Efficient Dynamic Ranking via Crowdsourcing JMLR 2016 Bayes-Optimal Effort Allocation in Crowdsourcing: Bounds and Index Policies AISTATS 2016 Blazing the trails before beating the path: Sample-efficient Monte-Carlo planning NeurIPS 2016 Improving PAC Exploration Using the Median Of Means NeurIPS 2016 Inverse Reinforcement Learning with Simultaneous Estimation of Rewards and Dynamics AISTATS 2016 Multi-Objective Markov Decision Processes for Data-Driven Decision Support JMLR 2016 Only H is left: Near-tight Episodic PAC RL NeurIPS 2016 Policy Error Bounds for Model-Based Reinforcement Learning with Factored Linear Models COLT 2016 Representing and Learning Complex Object Interactions RSS 2016 Safe Exploration in Finite Markov Decision Processes with Gaussian Processes NeurIPS 2016 Safe Policy Improvement by Minimizing Robust Baseline Regret NeurIPS 2016 Weak Convergence Properties of Constrained Emphatic Temporal-difference Learning with Constant and Slowly Diminishing Stepsize JMLR 2016