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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 21–30 of 1918 papers

TitleStatusHype
Deep Inverse Q-learning with ConstraintsCode1
Benchmarking Batch Deep Reinforcement Learning AlgorithmsCode1
Cal-QL: Calibrated Offline RL Pre-Training for Efficient Online Fine-TuningCode1
Reinforcement Learning in High-frequency Market MakingCode1
CCLF: A Contrastive-Curiosity-Driven Learning Framework for Sample-Efficient Reinforcement LearningCode1
A Deep Reinforcement Learning Approach for Finding Non-Exploitable Strategies in Two-Player Atari GamesCode1
Benchmarking Deep Graph Generative Models for Optimizing New Drug Molecules for COVID-19Code1
Conservative Q-Learning for Offline Reinforcement LearningCode1
Continuous Deep Q-Learning with Model-based AccelerationCode1
Backprop-Free Reinforcement Learning with Active Neural Generative CodingCode1
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