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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 1071–1080 of 1918 papers

TitleStatusHype
From r to Q^*: Your Language Model is Secretly a Q-Function—0
Frugal Reinforcement-based Active Learning—0
Full Gradient Deep Reinforcement Learning for Average-Reward Criterion—0
Functional Stability of Discounted Markov Decision Processes Using Economic MPC Dissipativity Theory—0
Gap-Dependent Bounds for Federated Q-learning—0
Gap-Dependent Bounds for Q-Learning using Reference-Advantage Decomposition—0
Gap-Dependent Bounds for Two-Player Markov Games—0
GenCos' Behaviors Modeling Based on Q Learning Improved by Dichotomy—0
Generative Multi-Agent Q-Learning for Policy Optimization: Decentralized Wireless Networks—0
Genetic Algorithm enhanced by Deep Reinforcement Learning in parent selection mechanism and mutation : Minimizing makespan in permutation flow shop scheduling problems—0
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