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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 551–600 of 1918 papers

TitleStatusHype
Artificial Intelligence and Algorithmic Price Collusion in Two-sided Markets—0
A General Framework for Learning Mean-Field Games—0
A General Control-Theoretic Approach for Reinforcement Learning: Theory and Algorithms—0
A Comparative Analysis of Deep Reinforcement Learning-enabled Freeway Decision-making for Automated Vehicles—0
Reinforcement Learning for an Efficient and Effective Malware Investigation during Cyber Incident Response—0
DDPG based on multi-scale strokes for financial time series trading strategy—0
Data-Efficient Quadratic Q-Learning Using LMIs—0
A Risk-Averse Preview-based Q-Learning Algorithm: Application to Highway Driving of Autonomous Vehicles—0
Data-efficient Deep Reinforcement Learning for Vehicle Trajectory Control—0
Data-efficient Deep Reinforcement Learning for Dexterous Manipulation—0
A Reinforcement Learning-Based Task Mapping Method to Improve the Reliability of Clustered Manycores—0
Data-Driven Knowledge Transfer in Batch Q^* Learning—0
Data-driven inventory management for new products: An adjusted Dyna-Q approach with transfer learning—0
A reinforcement learning based decision support system in textile manufacturing process—0
Data-Driven H-infinity Control with a Real-Time and Efficient Reinforcement Learning Algorithm: An Application to Autonomous Mobility-on-Demand Systems—0
Data-Based Efficient Off-Policy Stabilizing Optimal Control Algorithms for Discrete-Time Linear Systems via Damping Coefficients—0
A Reinforcement Learning Approach to Target Tracking in a Camera Network—0
A Framework for Provably Stable and Consistent Training of Deep Feedforward Networks—0
Adapting Double Q-Learning for Continuous Reinforcement Learning—0
DASA: Delay-Adaptive Multi-Agent Stochastic Approximation—0
A reinforcement learning approach to improve communication performance and energy utilization in fog-based IoT—0
Cycles and collusion in congestion games under Q-learning—0
Curriculum Q-Learning for Visual Vocabulary Acquisition—0
A Reinforcement Learning Approach to Dairy Farm Battery Management using Q Learning—0
Cross Learning in Deep Q-Networks—0
A Reinforcement Learning Approach to Parameter Selection for Distributed Optimal Power Flow—0
Criticality-Based Varying Step-Number Algorithm for Reinforcement Learning—0
Credit-cognisant reinforcement learning for multi-agent cooperation—0
A Reinforcement Learning Approach for Process Parameter Optimization in Additive Manufacturing—0
A Flexible Framework for Incorporating Patient Preferences Into Q-Learning—0
Actuator Trajectory Planning for UAVs with Overhead Manipulator using Reinforcement Learning—0
ACL-QL: Adaptive Conservative Level in Q-Learning for Offline Reinforcement Learning—0
Credit Assignment: Challenges and Opportunities in Developing Human-like AI Agents—0
Coverage-aware and Reinforcement Learning Using Multi-agent Approach for HD Map QoS in a Realistic Environment—0
A reinforcement learning algorithm for building collaboration in multi-agent systems—0
Coverage Analysis of Multi-Environment Q-Learning Algorithms for Wireless Network Optimization—0
Coverage Analysis for Digital Cousin Selection -- Improving Multi-Environment Q-Learning—0
A Finite Time Analysis of Temporal Difference Learning With Linear Function Approximation—0
Count-Based Temperature Scheduling for Maximum Entropy Reinforcement Learning—0
A Q-Learning-Based Topology-Aware Routing Protocol for Flying Ad Hoc Networks—0
Correlated Deep Q-learning based Microgrid Energy Management—0
Correct-by-synthesis reinforcement learning with temporal logic constraints—0
A Q-Learning-based Approach for Distributed Beam Scheduling in mmWave Networks—0
A Finite-Time Analysis of Q-Learning with Neural Network Function Approximation—0
CoordiQ : Coordinated Q-learning for Electric Vehicle Charging Recommendation—0
Coordinating Ride-Pooling with Public Transit using Reward-Guided Conservative Q-Learning: An Offline Training and Online Fine-Tuning Reinforcement Learning Framework—0
A Q-learning approach to the continuous control problem of robot inverted pendulum balancing—0
Cooperative Reward Shaping for Multi-Agent Pathfinding—0
Cooperative Optimal Output Tracking for Discrete-Time Multiagent Systems: Stabilizing Policy Iteration Frameworks and Analysis—0
A Q-learning Approach for Adherence-Aware Recommendations—0
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