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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 11–20 of 1918 papers

TitleStatusHype
ACE: Cooperative Multi-agent Q-learning with Bidirectional Action-DependencyCode2
Diffusion Policies as an Expressive Policy Class for Offline Reinforcement LearningCode2
Safe Multi-Agent Reinforcement Learning with Bilevel Optimization in Autonomous DrivingCode2
Digi-Q: Learning Q-Value Functions for Training Device-Control AgentsCode2
Boosting Soft Actor-Critic: Emphasizing Recent Experience without Forgetting the PastCode1
Boosting Continuous Control with Consistency PolicyCode1
Cal-QL: Calibrated Offline RL Pre-Training for Efficient Online Fine-TuningCode1
Benchmarking Batch Deep Reinforcement Learning AlgorithmsCode1
Believe What You See: Implicit Constraint Approach for Offline Multi-Agent Reinforcement LearningCode1
Benchmarking Deep Graph Generative Models for Optimizing New Drug Molecules for COVID-19Code1
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