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 851–900 of 1918 papers

TitleStatusHype
A Learning Based Framework for Handling Uncertain Lead Times in Multi-Product Inventory Management—0
Pessimistic Q-Learning for Offline Reinforcement Learning: Towards Optimal Sample Complexity—0
Whittle Index based Q-Learning for Wireless Edge Caching with Linear Function Approximation—0
Autonomous Warehouse Robot using Deep Q-Learning—0
PooL: Pheromone-inspired Communication Framework forLarge Scale Multi-Agent Reinforcement Learning—0
UAV Base Station Trajectory Optimization Based on Reinforcement Learning in Post-disaster Search and Rescue Operations—0
Goal Recognition as Reinforcement LearningCode0
Artificial Intelligence and Auction Design—0
Microservice Deployment in Edge Computing Based on Deep Q LearningCode1
Regularized Q-learning—0
Transferred Q-learning—0
Intelligent Autonomous Intersection Management—0
Multiple Correlated Jammers Nullification using LSTM-based Deep Dueling Neural Network—0
Stochastic Gradient Descent with Dependent Data for Offline Reinforcement Learning—0
A deep Q-learning method for optimizing visual search strategies in backgrounds of dynamic noise—0
Deep Q-learning: a robust control approachCode0
Optimal variance-reduced stochastic approximation in Banach spaces—0
Deep Reinforcement Learning with Spiking Q-learning—0
Addressing Maximization Bias in Reinforcement Learning with Two-Sample TestingCode1
A Family of Cognitively Realistic Parsing Environments for Deep Reinforcement Learning—0
Criticality-Based Varying Step-Number Algorithm for Reinforcement Learning—0
Task Independent Capsule-Based Agents for Deep Q-Learning—0
Age-of-information minimization via opportunistic sampling by an energy harvesting source—0
Sales Time Series Analytics Using Deep Q-Learning—0
Reinforcement Learning for Task Specifications with Action-Constraints—0
Operator Deep Q-Learning: Zero-Shot Reward Transferring in Reinforcement Learning—0
A Statistical Analysis of Polyak-Ruppert Averaged Q-learningCode0
A Graph Attention Learning Approach to Antenna Tilt Optimization—0
Task and Model Agnostic Adversarial Attack on Graph Neural NetworksCode0
Safety and Liveness Guarantees through Reach-Avoid Reinforcement LearningCode1
Aerial Base Station Positioning and Power Control for Securing Communications: A Deep Q-Network Approach—0
Amortized Noisy Channel Neural Machine Translation—0
Finite-Sample Analysis of Decentralized Q-Learning for Stochastic Games—0
Teaching a Robot to Walk Using Reinforcement Learning—0
Control-Tutored Reinforcement Learning: Towards the Integration of Data-Driven and Model-Based Control—0
Quantum Architecture Search via Continual Reinforcement Learning—0
High-Dimensional Stock Portfolio Trading with Deep Reinforcement Learning—0
Deep Q-Learning Market Makers in a Multi-Agent Simulated Stock Market—0
ShinRL: A Library for Evaluating RL Algorithms from Theoretical and Practical PerspectivesCode1
Convergence Results For Q-Learning With Experience Replay—0
Application of Deep Reinforcement Learning to Payment Fraud—0
Replay For Safety—0
Pragmatic Implementation of Reinforcement Algorithms For Path Finding On Raspberry Pi—0
A Risk-Averse Preview-based Q-Learning Algorithm: Application to Highway Driving of Autonomous Vehicles—0
Regularized Softmax Deep Multi-Agent Q-LearningCode1
Finite Sample Analysis of Average-Reward TD Learning and Q-Learning—0
Faster Non-asymptotic Convergence for Double Q-learning—0
Solving reward-collecting problems with UAVs: a comparison of online optimization and Q-learningCode0
Continuous Control With Ensemble Deep Deterministic Policy GradientsCode0
DeepCQ+: Robust and Scalable Routing with Multi-Agent Deep Reinforcement Learning for Highly Dynamic Networks—0
Show:102550
← PrevPage 18 of 39Next →

No leaderboard results yet.