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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 501–525 of 1918 papers

TitleStatusHype
Convex Q Learning in a Stochastic Environment: Extended Version—0
Multi Agent DeepRL based Joint Power and Subchannel Allocation in IAB networks—0
Physics-Based Trajectory Design for Cellular-Connected UAV in Rainy Environments Based on Deep Reinforcement Learning—0
Reinforcement Learning for Sampling on Temporal Medical Imaging SequencesCode0
Learning Visual Tracking and Reaching with Deep Reinforcement Learning on a UR10e Robotic ArmCode0
Traffic Light Control with Reinforcement LearningCode0
Actuator Trajectory Planning for UAVs with Overhead Manipulator using Reinforcement Learning—0
Towards Few-shot Coordination: Revisiting Ad-hoc Teamplay Challenge In the Game of HanabiCode0
Reinforcement Learning for Battery Management in Dairy Farming—0
Improving Sample Efficiency of Model-Free Algorithms for Zero-Sum Markov Games—0
On-demand Cold Start Frequency Reduction with Off-Policy Reinforcement Learning in Serverless Computing—0
A Comparison of Classical and Deep Reinforcement Learning Methods for HVAC Control—0
Variations on the Reinforcement Learning performance of BlackjackCode0
Deep Q-Network for Stochastic Process Environments—0
Unsynchronized Decentralized Q-Learning: Two Timescale Analysis By Persistence—0
Minimax Optimal Q Learning with Nearest Neighbors—0
Robust Multi-Agent Reinforcement Learning with State UncertaintyCode1
Stability of Multi-Agent Learning: Convergence in Network Games with Many Players—0
Parallel Q-Learning: Scaling Off-policy Reinforcement Learning under Massively Parallel SimulationCode0
Adversarial Agents For Attacking Inaudible Voice Activated Devices—0
A Flexible Framework for Incorporating Patient Preferences Into Q-Learning—0
Exploring reinforcement learning techniques for discrete and continuous control tasks in the MuJoCo environmentCode0
Distributed 3D-Beam Reforming for Hovering-Tolerant UAVs Communication over Coexistence: A Deep-Q Learning for Intelligent Space-Air-Ground Integrated Networks—0
Meta-Value Learning: a General Framework for Learning with Learning AwarenessCode0
Credit Assignment: Challenges and Opportunities in Developing Human-like AI Agents—0
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