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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 751–800 of 1918 papers

TitleStatusHype
Fictitious play in zero-sum stochastic games—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
Balancing Profit, Risk, and Sustainability for Portfolio Management—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
Deep Transfer Q-Learning for Offline Non-Stationary Reinforcement Learning—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
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
Balancing a CartPole System with Reinforcement Learning -- A Tutorial—0
ShiQ: Bringing back Bellman to LLMs—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
Balanced Q-learning: Combining the Influence of Optimistic and Pessimistic Targets—0
Deep Surrogate Q-Learning for Autonomous Driving—0
FRAC-Q-Learning: A Reinforcement Learning with Boredom Avoidance Processes for Social Robots—0
Almost Sure Convergence Rates and Concentration of Stochastic Approximation and Reinforcement Learning with Markovian Noise—0
From r to Q^*: Your Language Model is Secretly a Q-Function—0
Continuous Deep Q-Learning in Optimal Control Problems: Normalized Advantage Functions Analysis—0
Harnessing Deep Q-Learning for Enhanced Statistical Arbitrage in High-Frequency Trading: A Comprehensive Exploration—0
Full Gradient Deep Reinforcement Learning for Average-Reward Criterion—0
Functional Stability of Discounted Markov Decision Processes Using Economic MPC Dissipativity Theory—0
HAVER: Instance-Dependent Error Bounds for Maximum Mean Estimation and Applications to Q-Learning and Monte Carlo Tree Search—0
Continuous-time q-Learning for Jump-Diffusion Models under Tsallis Entropy—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
Continuous-time Risk-sensitive Reinforcement Learning via Quadratic Variation Penalty—0
Hidden Incentives for Auto-Induced Distributional Shift—0
Deep Spectral Q-learning with Application to Mobile Health—0
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