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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 751–775 of 1918 papers

TitleStatusHype
Balanced Q-learning: Combining the Influence of Optimistic and Pessimistic Targets—0
Fidelity-based Probabilistic Q-learning for Control of Quantum Systems—0
Final Adaptation Reinforcement Learning for N-Player Games—0
Finding the best design parameters for optical nanostructures using reinforcement learning—0
Finite Horizon Q-learning: Stability, Convergence, Simulations and an application on Smart Grids—0
Finite-Sample Analysis for SARSA with Linear Function Approximation—0
Finite Sample Analysis of Average-Reward TD Learning and Q-Learning—0
Finite-Sample Analysis of Decentralized Q-Learning for Stochastic Games—0
Deep Surrogate Q-Learning for Autonomous Driving—0
Finite-sample Guarantees for Nash Q-learning with Linear Function Approximation—0
Finite-Time Analysis for Double Q-learning—0
Finite-Time Analysis of Asynchronous Stochastic Approximation and Q-Learning—0
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
Almost Sure Convergence Rates and Concentration of Stochastic Approximation and Reinforcement Learning with Markovian Noise—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
CoNSoLe: Convex Neural Symbolic Learning—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
Gap-Dependent Bounds for Two-Player Markov Games—0
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