value function

Lipschitz Continuity in Model-based Reinforcement Learning ICML 2018 Meta-Gradient Reinforcement Learning NeurIPS 2018 Modeling Others using Oneself in Multi-Agent Reinforcement Learning ICML 2018 Policy and Value Transfer in Lifelong Reinforcement Learning ICML 2018 Regret Minimization for Partially Observable Deep Reinforcement Learning ICML 2018 Reinforcement Learning with Multiple Experts: A Bayesian Model Combination Approach NeurIPS 2018 Reward Estimation for Variance Reduction in Deep Reinforcement Learning CoRL 2018 rho-POMDPs have Lipschitz-Continuous epsilon-Optimal Value Functions NeurIPS 2018 Temporal Regularization for Markov Decision Process NeurIPS 2018 The Mirage of Action-Dependent Baselines in Reinforcement Learning ICML 2018 Transfer of Value Functions via Variational Methods NeurIPS 2018 TreeQN and ATreeC: Differentiable Tree-Structured Models for Deep Reinforcement Learning ICLR 2018 Bridging the Gap Between Value and Policy Based Reinforcement Learning NeurIPS 2017 Hybrid Reward Architecture for Reinforcement Learning NeurIPS 2017 Is the Bellman residual a bad proxy? NeurIPS 2017 Neural Episodic Control ICML 2017 Stabilising Experience Replay for Deep Multi-Agent Reinforcement Learning ICML 2017 Stochastic Variance Reduction Methods for Policy Evaluation ICML 2017 Successor Features for Transfer in Reinforcement Learning NeurIPS 2017 The Predictron: End-To-End Learning and Planning ICML 2017 Value-Aware Loss Function for Model-based Reinforcement Learning AISTATS 2017 Weighted Double Q-learning IJCAI 2017 Addressing Environment Non-Stationarity by Repeating Q-learning Updates JMLR 2016 An Emphatic Approach to the Problem of Off-policy Temporal-Difference Learning JMLR 2016 Blazing the trails before beating the path: Sample-efficient Monte-Carlo planning NeurIPS 2016