Value Iteration

A General Offline Reinforcement Learning Framework for Interactive Recommendation AAAI 2021 A Universal 2-state n-action Adaptive Management Solver AAAI 2021 Average-Reward Learning and Planning with Options NeurIPS 2021 Bootstrapping Fitted Q-Evaluation for Off-Policy Inference ICML 2021 BooVI: Provably Efficient Bootstrapped Value Iteration NeurIPS 2021 Certification of iterative predictions in Bayesian neural networks UAI 2021 DeepTrader: A Deep Reinforcement Learning Approach for Risk-Return Balanced Portfolio Management with Market Conditions Embedding AAAI 2021 Fast Approximate Dynamic Programming for Infinite-Horizon Markov Decision Processes NeurIPS 2021 Finding the Stochastic Shortest Path with Low Regret: the Adversarial Cost and Unknown Transition Case ICML 2021 Foresee then Evaluate: Decomposing Value Estimation with Latent Future Prediction AAAI 2021 Generalize a Small Pre-trained Model to Arbitrarily Large TSP Instances AAAI 2021 Geometric Value Iteration: Dynamic Error-Aware KL Regularization for Reinforcement Learning ACML 2021 Hamilton-Jacobi Deep Q-Learning for Deterministic Continuous-Time Systems with Lipschitz Continuous Controls JMLR 2021 Improved Variance-Aware Confidence Sets for Linear Bandits and Linear Mixture MDP NeurIPS 2021 Learning MDPs from Features: Predict-Then-Optimize for Sequential Decision Making by Reinforcement Learning NeurIPS 2021 Learning Space Partitions for Path Planning NeurIPS 2021 Loop Estimator for Discounted Values in Markov Reward Processes AAAI 2021 Minimax Regret for Stochastic Shortest Path NeurIPS 2021 Minimax Regret Optimisation for Robust Planning in Uncertain Markov Decision Processes AAAI 2021 Monte Carlo Tree Search With Iteratively Refining State Abstractions NeurIPS 2021 Neural Algorithmic Reasoners are Implicit Planners NeurIPS 2021 No-Press Diplomacy from Scratch NeurIPS 2021 PID Accelerated Value Iteration Algorithm ICML 2021 Policy Caches with Successor Features ICML 2021 Preferential Temporal Difference Learning ICML 2021