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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 1561–1570 of 1918 papers

TitleStatusHype
Towards Empathic Deep Q-LearningCode0
Q-Learning Inspired Self-Tuning for Energy Efficiency in HPC—0
In Hindsight: A Smooth Reward for Steady Exploration—0
Deceptive Reinforcement Learning Under Adversarial Manipulations on Cost Signals—0
Optimal Use of Experience in First Person Shooter Environments—0
Neural networks with motivation—0
Reinforcement Learning-Based Trajectory Design for the Aerial Base Stations—0
Cache-Aided NOMA Mobile Edge Computing: A Reinforcement Learning Approach—0
Reward Prediction Error as an Exploration Objective in Deep RL—0
A Generalized Minimax Q-learning Algorithm for Two-Player Zero-Sum Stochastic Games—0
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