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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 141–150 of 1918 papers

TitleStatusHype
Deep Reinforcement Learning-based Intelligent Traffic Signal Controls with Optimized CO2 emissionsCode1
Deep Reinforcement Learning with Double Q-learningCode1
When should we prefer Decision Transformers for Offline Reinforcement Learning?Code1
Diffusion Policies creating a Trust Region for Offline Reinforcement LearningCode1
Discriminator Soft Actor Critic without Extrinsic RewardsCode1
Distilling Reinforcement Learning Tricks for Video GamesCode1
IDQL: Implicit Q-Learning as an Actor-Critic Method with Diffusion PoliciesCode1
Offline RL with No OOD Actions: In-Sample Learning via Implicit Value RegularizationCode1
EpidemiOptim: A Toolbox for the Optimization of Control Policies in Epidemiological ModelsCode1
Uncertainty Weighted Actor-Critic for Offline Reinforcement LearningCode1
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