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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 501–550 of 1918 papers

TitleStatusHype
Conservative and Risk-Aware Offline Multi-Agent Reinforcement LearningCode0
Enhanced Deep Q-Learning for 2D Self-Driving Cars: Implementation and Evaluation on a Custom Track Environment—0
Leveraging Digital Cousins for Ensemble Q-Learning in Large-Scale Wireless NetworksCode0
Solving Deep Reinforcement Learning Tasks with Evolution Strategies and Linear Policy NetworksCode0
ORIENT: A Priority-Aware Energy-Efficient Approach for Latency-Sensitive Applications in 6G—0
Federated Deep Q-Learning and 5G load balancing—0
Value function interference and greedy action selection in value-based multi-objective reinforcement learning—0
Attention-Enhanced Prioritized Proximal Policy Optimization for Adaptive Edge Caching—0
Enhancement of High-definition Map Update Service Through Coverage-aware and Reinforcement Learning—0
Federated Offline Reinforcement Learning: Collaborative Single-Policy Coverage Suffices—0
Multi-Timescale Ensemble Q-learning for Markov Decision Process Policy OptimizationCode0
A Deep Reinforcement Learning Approach for Adaptive Traffic Routing in Next-gen Networks—0
Logical Specifications-guided Dynamic Task Sampling for Reinforcement Learning AgentsCode0
Diffusion World Model: Future Modeling Beyond Step-by-Step Rollout for Offline Reinforcement Learning—0
Multi-Agent Reinforcement Learning for Offloading Cellular Communications with Cooperating UAVs—0
SQT -- std Q-target—0
MinMaxMin Q-learning—0
DRL-Based Dynamic Channel Access and SCLAR Maximization for Networks Under Jamming—0
Deep Robot Sketching: An application of Deep Q-Learning Networks for human-like sketching—0
RadDQN: a Deep Q Learning-based Architecture for Finding Time-efficient Minimum Radiation Exposure PathwayCode0
FM3Q: Factorized Multi-Agent MiniMax Q-Learning for Two-Team Zero-Sum Markov Game—0
Nash Soft Actor-Critic LEO Satellite Handover Management Algorithm for Flying Vehicles—0
Scheduled Curiosity-Deep Dyna-Q: Efficient Exploration for Dialog Policy Learning—0
Extrinsicaly Rewarded Soft Q Imitation Learning with Discriminator—0
Emergence of cooperation under punishment: A reinforcement learning perspective—0
Regularized Q-Learning with Linear Function Approximation—0
Constant Stepsize Q-learning: Distributional Convergence, Bias and Extrapolation—0
VQC-Based Reinforcement Learning with Data Re-uploading: Performance and TrainabilityCode0
Information-Theoretic State Variable Selection for Reinforcement LearningCode0
REValueD: Regularised Ensemble Value-Decomposition for Factorisable Markov Decision Processes—0
A Semantic-Aware Multiple Access Scheme for Distributed, Dynamic 6G-Based ApplicationsCode0
Graph Q-Learning for Combinatorial Optimization—0
Model-Free Reinforcement Learning for Automated Fluid Administration in Critical Care—0
Advancing ECG Diagnosis Using Reinforcement Learning on Global Waveform Variations Related to P Wave and PR Interval—0
Deep Reinforcement Multi-agent Learning framework for Information Gathering with Local Gaussian Processes for Water Monitoring—0
Decision Making in Non-Stationary Environments with Policy-Augmented SearchCode0
An Empirical Investigation of Value-Based Multi-objective Reinforcement Learning for Stochastic Environments—0
SPQR: Controlling Q-ensemble Independence with Spiked Random Model for Reinforcement LearningCode0
A Deep Q-Learning based Smart Scheduling of EVs for Demand Response in Smart Grids—0
The Best Time for an Update: Risk-Sensitive Minimization of Age-Based Metrics—0
Personalized Dynamic Pricing Policy for Electric Vehicles: Reinforcement learning approach—0
Dynamic Decision Making in Engineering System Design: A Deep Q-Learning Approach—0
Reinforcement Learning for Safe Occupancy Strategies in Educational Spaces during an Epidemic—0
Distributional Reinforcement Learning-based Energy Arbitrage Strategies in Imbalance Settlement Mechanism—0
Federated Q-Learning: Linear Regret Speedup with Low Communication Cost—0
Maximum entropy GFlowNets with soft Q-learning—0
Optimal coordination of resources: A solution from reinforcement learning—0
Stability of Multi-Agent Learning in Competitive Networks: Delaying the Onset of Chaos—0
Investigating the Performance and Reliability, of the Q-Learning Algorithm in Various Unknown EnvironmentsCode0
Sample Efficient Reinforcement Learning with Partial Dynamics KnowledgeCode0
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