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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 31–40 of 1918 papers

TitleStatusHype
Coarse-to-Fine Q-attention: Efficient Learning for Visual Robotic Manipulation via DiscretisationCode1
Conservative Q-Learning for Offline Reinforcement LearningCode1
Benchmarking Deep Graph Generative Models for Optimizing New Drug Molecules for COVID-19Code1
Backprop-Free Reinforcement Learning with Active Neural Generative CodingCode1
When should we prefer Decision Transformers for Offline Reinforcement Learning?Code1
Believe What You See: Implicit Constraint Approach for Offline Multi-Agent Reinforcement LearningCode1
Benchmarking Batch Deep Reinforcement Learning AlgorithmsCode1
Acting in Delayed Environments with Non-Stationary Markov PoliciesCode1
Cal-QL: Calibrated Offline RL Pre-Training for Efficient Online Fine-TuningCode1
Boosting Continuous Control with Consistency PolicyCode1
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