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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 801–825 of 1918 papers

TitleStatusHype
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
Challenging On Car Racing Problem from OpenAI gym—0
GINO-Q: Learning an Asymptotically Optimal Index Policy for Restless Multi-armed Bandits—0
G-Learner and GIRL: Goal Based Wealth Management with Reinforcement Learning—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
Enhancement of High-definition Map Update Service Through Coverage-aware and Reinforcement Learning—0
Censored Deep Reinforcement Patrolling with Information Criterion for Monitoring Large Water Resources using Autonomous Surface Vehicles—0
Enhanced Rolling Horizon Evolution Algorithm with Opponent Model Learning: Results for the Fighting Game AI Competition—0
GraMeR: Graph Meta Reinforcement Learning for Multi-Objective Influence Maximization—0
Enhanced Q-Learning Approach to Finite-Time Reachability with Maximum Probability for Probabilistic Boolean Control Networks—0
Graph-based Reinforcement Learning meets Mixed Integer Programs: An application to 3D robot assembly discovery—0
Graph Exploration for Effective Multi-agent Q-Learning—0
Cellular traffic offloading via Opportunistic Networking with Reinforcement Learning—0
Graph Q-Learning for Combinatorial Optimization—0
Greedy-Step Off-Policy Reinforcement Learning—0
Greedy UnMixing for Q-Learning in Multi-Agent Reinforcement Learning—0
A new multilayer optical film optimal method based on deep q-learning—0
A Deep Reinforcement Learning Architecture for Multi-stage Optimal Control—0
Growing Q-Networks: Solving Continuous Control Tasks with Adaptive Control Resolution—0
Guiding Reinforcement Learning Exploration Using Natural Language—0
On Using Hamiltonian Monte Carlo Sampling for Reinforcement Learning Problems in High-dimension—0
Hamilton-Jacobi-Bellman Equations for Q-Learning in Continuous Time—0
Action Learning for 3D Point Cloud Based Organ Segmentation—0
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