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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 1631–1640 of 1918 papers

TitleStatusHype
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
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