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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 1626–1650 of 1918 papers

TitleStatusHype
Long and Short Memory Balancing in Visual Co-Tracking using Q-Learning—0
Sample-Optimal Parametric Q-Learning Using Linearly Additive Features—0
Learning Best Response Strategies for Agents in Ad Exchanges—0
Dynamic-Weighted Simplex Strategy for Learning Enabled Cyber Physical SystemsCode0
Finite-Sample Analysis for SARSA with Linear Function Approximation—0
A Theory of Regularized Markov Decision Processes—0
Privacy-preserving Q-Learning with Functional Noise in Continuous State SpacesCode0
Making Deep Q-learning methods robust to time discretizationCode0
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
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