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Value Iteration

306 directly classified papers

Papers per year

Papers

Robust Value Iteration for Continuous Control Tasks RSS 2021
Average-Reward Learning and Planning with Options NIPS 2021
Self-Paced Context Evaluation for Contextual Reinforcement Learning ICML 2021
Minimax Regret for Stochastic Shortest Path NIPS 2021
Reinforcement Learning for Abstractive Question Summarization with Question-aware Semantic Rewards IJCNLP 2021
Improved Variance-Aware Confidence Sets for Linear Bandits and Linear Mixture MDP NIPS 2021
Point-Based Methods for Model Checking in Partially Observable Markov Decision Processes AAAI 2020
Off-Policy Evaluation in Partially Observable Environments AAAI 2020
Task Scoping for Efficient Planning in Open Worlds (Student Abstract) AAAI 2020
Reinforcement-Learning Based Portfolio Management with Augmented Asset Movement Prediction States AAAI 2020
No-regret Exploration in Contextual Reinforcement Learning UAI 2020
Approximate Inference in Discrete Distributions with Monte Carlo Tree Search and Value Functions AISTATS 2020
AsyncQVI: Asynchronous-Parallel Q-Value Iteration for Discounted Markov Decision Processes with Near-Optimal Sample Complexity AISTATS 2020
Bayesian Reinforcement Learning via Deep, Sparse Sampling AISTATS 2020
Value Preserving State-Action Abstractions AISTATS 2020
Learning Near Optimal Policies with Low Inherent Bellman Error ICML 2020
Data Valuation using Reinforcement Learning ICML 2020
Breaking the Curse of Many Agents: Provable Mean Embedding Q-Iteration for Mean-Field Reinforcement Learning ICML 2020
Near-optimal Regret Bounds for Stochastic Shortest Path ICML 2020
Structured Policy Iteration for Linear Quadratic Regulator ICML 2020
Learning with Good Feature Representations in Bandits and in RL with a Generative Model ICML 2020
Efficiently Solving MDPs with Stochastic Mirror Descent ICML 2020
Reward-Free Exploration for Reinforcement Learning ICML 2020
On the Expressivity of Neural Networks for Deep Reinforcement Learning ICML 2020
Tightening Exploration in Upper Confidence Reinforcement Learning ICML 2020