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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 1181–1190 of 1918 papers

TitleStatusHype
Representation Learning for Context-Dependent Decision-Making—0
Representing Entropy : A short proof of the equivalence between soft Q-learning and policy gradients—0
Reputation Bootstrapping for Composite Services using CP-nets—0
Residual Policy Gradient: A Reward View of KL-regularized Objective—0
Residual Q-Learning: Offline and Online Policy Customization without Value—0
Resilient UAV Trajectory Planning via Few-Shot Meta-Offline Reinforcement Learning—0
The state-of-the-art review on resource allocation problem using artificial intelligence methods on various computing paradigms—0
REValueD: Regularised Ensemble Value-Decomposition for Factorisable Markov Decision Processes—0
Reverse Experience Replay—0
Reversible Action Design for Combinatorial Optimization with Reinforcement Learning—0
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