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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 901–910 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 a particle dynamics environment applied to emergency evacuation of a room with obstacles—0
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