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Q-Learning

The goal of Q-learning is to learn a policy, which tells an agent what action to take under what circumstances.

( Image credit: Playing Atari with Deep Reinforcement Learning )

Papers

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Showing 1651–1675 of 1918 papers

TitleStatusHype
Q-learning with UCB Exploration is Sample Efficient for Infinite-Horizon MDP—0
Provably efficient RL with Rich Observations via Latent State DecodingCode0
Combinational Q-Learning for Dou Di ZhuCode0
Reinforcement Learning of Markov Decision Processes with Peak Constraints—0
Distillation Strategies for Proximal Policy Optimization—0
Understanding Multi-Step Deep Reinforcement Learning: A Systematic Study of the DQN TargetCode0
A Deep Recurrent Q Network towards Self-adapting Distributed Microservices architectureCode0
Deep Reinforcement Learning for Imbalanced ClassificationCode0
Accelerating Goal-Directed Reinforcement Learning by Model Characterization—0
Optimal Decision-Making in Mixed-Agent Partially Observable Stochastic Environments via Reinforcement Learning—0
Adversarial Learning of a Sampler Based on an Unnormalized DistributionCode0
A Theoretical Analysis of Deep Q-Learning—0
Information-Directed Exploration for Deep Reinforcement LearningCode0
Reinforcement Learning for Adaptive Caching with Dynamic Storage Pricing—0
Double Deep Q-Learning for Optimal Execution—0
Learning Sharing Behaviors with Arbitrary Numbers of Agents—0
A new multilayer optical film optimal method based on deep q-learning—0
Active Deep Q-learning with Demonstration—0
Revisiting the Softmax Bellman Operator: New Benefits and New PerspectiveCode0
Non-delusional Q-learning and value-iteration—0
Urban Driving with Multi-Objective Deep Reinforcement LearningCode0
Reinforcement Learning with A* and a Deep HeuristicCode0
Switch-based Active Deep Dyna-Q: Efficient Adaptive Planning for Task-Completion Dialogue Policy LearningCode0
Emergence of Addictive Behaviors in Reinforcement Learning Agents—0
Deep Q learning for fooling neural networksCode0
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