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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 901–950 of 1918 papers

TitleStatusHype
Inverse Policy Evaluation for Value-based Sequential Decision-making—0
Decentralized Cooperative Multi-Agent Reinforcement Learning with Exploration—0
Inverse RL Scene Dynamics Learning for Nonlinear Predictive Control in Autonomous Vehicles—0
Investigating Reinforcement Learning Agents for Continuous State Space Environments—0
Investigating the Edge of Stability Phenomenon in Reinforcement Learning—0
Decentralized Microgrid Energy Management: A Multi-agent Correlated Q-learning Approach—0
Investigating the Properties of Neural Network Representations in Reinforcement Learning—0
Decentralized model-free reinforcement learning in stochastic games with average-reward objective—0
IoT-Aerial Base Station Task Offloading with Risk-Sensitive Reinforcement Learning for Smart Agriculture—0
Deep Reinforcement Learning with Discrete Normalized Advantage Functions for Resource Management in Network Slicing—0
Deep reinforcement learning with automated label extraction from clinical reports accurately classifies 3D MRI brain volumes—0
Decentralized Multi-Robot Formation Control Using Reinforcement Learning—0
Is Q-learning an Ill-posed Problem?—0
Autonomous Vehicle Fleet Coordination With Deep Reinforcement Learning—0
Is Q-Learning Provably Efficient? An Extended Analysis—0
Is Risk-Sensitive Reinforcement Learning Properly Resolved?—0
"Jam Me If You Can'': Defeating Jammer with Deep Dueling Neural Network Architecture and Ambient Backscattering Augmented Communications—0
Decentralized Semantic Traffic Control in AVs Using RL and DQN for Dynamic Roadblocks—0
Joint Inference of Reward Machines and Policies for Reinforcement Learning—0
Joint Learning of Interactive Spoken Content Retrieval and Trainable User Simulator—0
Joint Learning of Reward Machines and Policies in Environments with Partially Known Semantics—0
Decision-making at Unsignalized Intersection for Autonomous Vehicles: Left-turn Maneuver with Deep Reinforcement Learning—0
Joint User Association, Interference Cancellation and Power Control for Multi-IRS Assisted UAV Communications—0
KAN v.s. MLP for Offline Reinforcement Learning—0
Kernel-Based Distributed Q-Learning: A Scalable Reinforcement Learning Approach for Dynamic Treatment Regimes—0
Knowledge-Informed Auto-Penetration Testing Based on Reinforcement Learning with Reward Machine—0
Koopman Q-learning: Offline Reinforcement Learning via Symmetries of Dynamics—0
K-spin Hamiltonian for quantum-resolvable Markov decision processes—0
Language Inference with Multi-head Automata through Reinforcement Learning—0
Large-Scale Traffic Signal Control Using a Novel Multi-Agent Reinforcement Learning—0
Deep Reinforcement Learning with Attention for Slate Markov Decision Processes with High-Dimensional States and Actions—0
Late Breaking Results: Breaking Symmetry- Unconventional Placement of Analog Circuits using Multi-Level Multi-Agent Reinforcement Learning—0
Deep reinforcement learning with a particle dynamics environment applied to emergency evacuation of a room with obstacles—0
Learning agents with prioritization and parameter noise in continuous state and action space—0
Autonomous Vehicle Decision-Making Framework for Considering Malicious Behavior at Unsignalized Intersections—0
Age of Information Minimization using Multi-agent UAVs based on AI-Enhanced Mean Field Resource Allocation—0
Learning Augmented Index Policy for Optimal Service Placement at the Network Edge—0
Learning Automata Based Q-learning for Content Placement in Cooperative Caching—0
Learning-Based Joint User-AP Association and Resource Allocation in Ultra Dense Network—0
Learning-Based Strategy Design for Robot-Assisted Reminiscence Therapy Based on a Developed Model for People with Dementia—0
Learning Best Response Strategies for Agents in Ad Exchanges—0
Learning Control for Air Hockey Striking using Deep Reinforcement Learning—0
Learning Dexterous Manipulation from Suboptimal Experts—0
Learning Dialog Policies from Weak Demonstrations—0
Learning Efficient Parameter Server Synchronization Policies for Distributed SGD—0
Learning Explicit Credit Assignment for Multi-agent Joint Q-learning—0
Deep hierarchical reinforcement agents for automated penetration testing—0
Learning from Peers: Deep Transfer Reinforcement Learning for Joint Radio and Cache Resource Allocation in 5G RAN Slicing—0
Algorithmic Trading with Fitted Q Iteration and Heston Model—0
Autonomous Penetration Testing using Reinforcement Learning—0
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