SOTAVerified

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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 1576–1600 of 1918 papers

TitleStatusHype
Neural Temporal-Difference and Q-Learning Provably Converge to Global OptimaCode0
Adaptive Symmetric Reward Noising for Reinforcement LearningCode0
Deep Q-Learning with Q-Matrix Transfer Learning for Novel Fire Evacuation Environment—0
Stochastic Variance Reduction for Deep Q-learning—0
Deep Reinforcement Learning Based Parameter Control in Differential EvolutionCode0
Reinforcement Learning for Learning of Dynamical Systems in Uncertain Environment: a Tutorial—0
QBSO-FS: A Reinforcement Learning Based Bee Swarm Optimization Metaheuristic for Feature SelectionCode0
Reinforcement Learning for Robotics and Control with Active Uncertainty Reduction—0
Autonomous Penetration Testing using Reinforcement Learning—0
Stochastic approximation with cone-contractive operators: Sharp _-bounds for Q-learningCode0
Domain Adversarial Reinforcement Learning for Partial Domain Adaptation—0
Design of Artificial Intelligence Agents for Games using Deep Reinforcement Learning—0
Pretrain Soft Q-Learning with Imperfect Demonstrations—0
A Reinforcement Learning Perspective on the Optimal Control of Mutation Probabilities for the (1+1) Evolutionary Algorithm: First Results on the OneMax Problem—0
Toward Packet Routing with Fully-distributed Multi-agent Deep Reinforcement Learning—0
Accelerated Target Updates for Q-learning—0
Comprehensible Context-driven Text Game PlayingCode0
Deep Ordinal Reinforcement LearningCode0
Efficient Model-free Reinforcement Learning in Metric SpacesCode0
Learning agents with prioritization and parameter noise in continuous state and action space—0
Two-Timescale Networks for Nonlinear Value Function Approximation—0
Soft Q-Learning with Mutual-Information Regularization—0
A Deep Q-Learning Method for Downlink Power Allocation in Multi-Cell Networks—0
Zap Q-Learning for Optimal Stopping Time Problems—0
Target-Based Temporal Difference Learning—0
Show:102550
← PrevPage 64 of 77Next →

No leaderboard results yet.