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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 1051–1075 of 1918 papers

TitleStatusHype
A Discrete-Time Switching System Analysis of Q-learning—0
Finite-Time Analysis of Asynchronous Q-learning under Diminishing Step-Size from Control-Theoretic View—0
Final Iteration Convergence Bound of Q-Learning: Switching System Approach—0
Finite-Time Convergence Rates of Decentralized Stochastic Approximation with Applications in Multi-Agent and Multi-Task Learning—0
Finite-Time Analysis of Minimax Q-Learning for Two-Player Zero-Sum Markov Games: Switching System Approach—0
Finite-Time Analysis of Simultaneous Double Q-learning—0
Finite-Time Analysis of Whittle Index based Q-Learning for Restless Multi-Armed Bandits with Neural Network Function Approximation—0
Finite-Time Bounds for Two-Time-Scale Stochastic Approximation with Arbitrary Norm Contractions and Markovian Noise—0
Finite-Time Error Analysis of Online Model-Based Q-Learning with a Relaxed Sampling Model—0
Finite-Time Error Analysis of Soft Q-Learning: Switching System Approach—0
FIRE: A Failure-Adaptive Reinforcement Learning Framework for Edge Computing Migrations—0
Fire Threat Detection From Videos with Q-Rough Sets—0
Fitted Q-Learning for Relational Domains—0
Learning in Discounted-cost and Average-cost Mean-field Games—0
Fixed-Horizon Temporal Difference Methods for Stable Reinforcement Learning—0
Floyd-Warshall Reinforcement Learning: Learning from Past Experiences to Reach New Goals—0
FM3Q: Factorized Multi-Agent MiniMax Q-Learning for Two-Team Zero-Sum Markov Game—0
Forecasting and stabilizing chaotic regimes in two macroeconomic models via artificial intelligence technologies and control methods—0
FPGA Architecture for Deep Learning and its application to Planetary Robotics—0
FRAC-Q-Learning: A Reinforcement Learning with Boredom Avoidance Processes for Social Robots—0
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
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