value iteration

Pessimism Meets Invariance: Provably Efficient Offline Mean-Field Multi-Agent RL NeurIPS 2021 PID Accelerated Value Iteration Algorithm ICML 2021 Policy Caches with Successor Features ICML 2021 Provably Efficient Causal Reinforcement Learning with Confounded Observational Data NeurIPS 2021 Quantum algorithms for reinforcement learning with a generative model ICML 2021 Rapid Task-Solving in Novel Environments ICLR 2021 Reconciling Rewards with Predictive State Representations IJCAI 2021 Robust Value Iteration for Continuous Control Tasks RSS 2021 Scalable First-Order Methods for Robust MDPs AAAI 2021 Self-Paced Context Evaluation for Contextual Reinforcement Learning ICML 2021 Solving Partially Observable Stochastic Shortest-Path Games IJCAI 2021 Stochastic Shortest Path: Minimax, Parameter-Free and Towards Horizon-Free Regret NeurIPS 2021 Successor Feature Sets: Generalizing Successor Representations Across Policies AAAI 2021 Understanding End-to-End Model-Based Reinforcement Learning Methods as Implicit Parameterization NeurIPS 2021 When to stop value iteration: stability and near-optimality versus computation L4DC 2021 Adaptive Discretization for Model-Based Reinforcement Learning NeurIPS 2020 AsyncQVI: Asynchronous-Parallel Q-Value Iteration for Discounted Markov Decision Processes with Near-Optimal Sample Complexity AISTATS 2020 Learning Near Optimal Policies with Low Inherent Bellman Error ICML 2020 Leverage the Average: an Analysis of KL Regularization in Reinforcement Learning NeurIPS 2020 Momentum in Reinforcement Learning AISTATS 2020 Multi-Agent Routing Value Iteration Network ICML 2020 Multi-step Greedy Reinforcement Learning Algorithms ICML 2020 Off-Policy Interval Estimation with Lipschitz Value Iteration NeurIPS 2020 On the Convergence of Smooth Regularized Approximate Value Iteration Schemes NeurIPS 2020 Point-Based Methods for Model Checking in Partially Observable Markov Decision Processes AAAI 2020