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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 776–800 of 1918 papers

TitleStatusHype
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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