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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
Equivalence Between Policy Gradients and Soft Q-Learning—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
Episodic Exploration for Deep Deterministic Policies: An Application to StarCraft Micromanagement Tasks—0
Finite-Sample Analysis of Decentralized Q-Learning for Stochastic Games—0
Environment Transformer and Policy Optimization for Model-Based Offline Reinforcement Learning—0
Finite-sample Guarantees for Nash Q-learning with Linear Function Approximation—0
Chrome Dino Run using Reinforcement Learning—0
Finite-Time Analysis of Asynchronous Stochastic Approximation and Q-Learning—0
Entropy-Augmented Entropy-Regularized Reinforcement Learning and a Continuous Path from Policy Gradient to Q-Learning—0
Finite-Time Analysis of Asynchronous Q-learning under Diminishing Step-Size from Control-Theoretic View—0
Entropic Risk Optimization in Discounted MDPs: Sample Complexity Bounds with a Generative Model—0
Finite-Time Convergence Rates of Decentralized Stochastic Approximation with Applications in Multi-Agent and Multi-Task Learning—0
Chemoreception and chemotaxis of a three-sphere swimmer—0
Ensemble Bootstrapping for Q-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
Characterizing the Action-Generalization Gap in Deep Q-Learning—0
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