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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
An Independent Study of Reinforcement Learning and Autonomous Driving—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
A Deep Reinforcement Learning Trader without Offline Training—0
Finite-Sample Analysis of Decentralized Q-Learning for Stochastic Games—0
Action-modulated midbrain dopamine activity arises from distributed control policies—0
Finite-sample Guarantees for Nash Q-learning with Linear Function Approximation—0
Accelerated Target Updates for Q-learning—0
Finite-Time Analysis of Asynchronous Stochastic Approximation and Q-Learning—0
Equivalence Between Policy Gradients and Soft Q-Learning—0
Finite-Time Analysis of Asynchronous Q-learning under Diminishing Step-Size from Control-Theoretic View—0
Episodic Exploration for Deep Deterministic Policies: An Application to StarCraft Micromanagement Tasks—0
Finite-Time Convergence Rates of Decentralized Stochastic Approximation with Applications in Multi-Agent and Multi-Task Learning—0
Environment Transformer and Policy Optimization for Model-Based Offline Reinforcement Learning—0
Chrome Dino Run using Reinforcement 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
Entropy-Augmented Entropy-Regularized Reinforcement Learning and a Continuous Path from Policy Gradient to Q-Learning—0
Entropic Risk Optimization in Discounted MDPs: Sample Complexity Bounds with a Generative Model—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
Chemoreception and chemotaxis of a three-sphere swimmer—0
FPGA Architecture for Deep Learning and its application to Planetary Robotics—0
Ensemble Bootstrapping for Q-Learning—0
Characterizing the Action-Generalization Gap in Deep Q-Learning—0
From r to Q^*: Your Language Model is Secretly a Q-Function—0
An FPGA-Based On-Device Reinforcement Learning Approach using Online Sequential Learning—0
A Deep Reinforcement Learning Framework for Contention-Based Spectrum Sharing—0
Full Gradient Deep Reinforcement Learning for Average-Reward Criterion—0
Channel Estimation via Successive Denoising in MIMO OFDM Systems: A Reinforcement Learning Approach—0
Enhancing reinforcement learning by a finite reward response filter with a case study in intelligent structural control—0
Enhancing Q-Learning with Large Language Model Heuristics—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
Challenging On Car Racing Problem from OpenAI gym—0
An Experimental Comparison Between Temporal Difference and Residual Gradient with Neural Network Approximation—0
Enhancing Classification Performance via Reinforcement Learning for Feature Selection—0
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