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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 971–980 of 1918 papers

TitleStatusHype
Personalized Dynamic Pricing Policy for Electric Vehicles: Reinforcement learning approach—0
Personalized Medical Treatments Using Novel Reinforcement Learning Algorithms—0
Pessimistic Q-Learning for Offline Reinforcement Learning: Towards Optimal Sample Complexity—0
Photonic architecture for reinforcement learning—0
Physics-Based Trajectory Design for Cellular-Connected UAV in Rainy Environments Based on Deep Reinforcement Learning—0
PickLLM: Context-Aware RL-Assisted Large Language Model Routing—0
PID Accelerated Temporal Difference Algorithms—0
Planning and Learning in Average Risk-aware MDPs—0
Planning and Learning with Stochastic Action Sets—0
Planning Irregular Object Packing via Hierarchical Reinforcement Learning—0
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