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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 811–820 of 1918 papers

TitleStatusHype
Parallel bandit architecture based on laser chaos for reinforcement learning—0
Efficient Off-Policy Reinforcement Learning via Brain-Inspired Computing—0
Representation Learning for Context-Dependent Decision-Making—0
Final Iteration Convergence Bound of Q-Learning: Switching System Approach—0
Characterizing the Action-Generalization Gap in Deep Q-Learning—0
Neuromimetic Linear Systems -- Resilience and Learning—0
Simultaneous Double Q-learning with Conservative Advantage Learning for Actor-Critic MethodsCode0
Vehicle management in a modular production context using Deep Q-Learning—0
Chemoreception and chemotaxis of a three-sphere swimmer—0
CCLF: A Contrastive-Curiosity-Driven Learning Framework for Sample-Efficient Reinforcement LearningCode1
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