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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
IQ-Learn: Inverse soft-Q Learning for ImitationCode1
Is Q-learning Provably Efficient?Code1
When should we prefer Decision Transformers for Offline Reinforcement Learning?Code1
Benchmarking Batch Deep Reinforcement Learning AlgorithmsCode1
Believe What You See: Implicit Constraint Approach for Offline Multi-Agent Reinforcement LearningCode1
MADiff: Offline Multi-agent Learning with Diffusion ModelsCode1
Benchmarking Deep Graph Generative Models for Optimizing New Drug Molecules for COVID-19Code1
Boosting Soft Actor-Critic: Emphasizing Recent Experience without Forgetting the PastCode1
Boosting Continuous Control with Consistency PolicyCode1
Uncertainty Weighted Actor-Critic for Offline Reinforcement LearningCode1
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