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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 1651–1660 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
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