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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 1851–1875 of 1918 papers

TitleStatusHype
The Game Imitation: Deep Supervised Convolutional Networks for Quick Video Game AI—0
Collaborative Deep Reinforcement Learning for Joint Object Search—0
FPGA Architecture for Deep Learning and its application to Planetary Robotics—0
Learning to predict where to look in interactive environments using deep recurrent q-learning—0
Playing Doom with SLAM-Augmented Deep Reinforcement LearningCode0
Designing Neural Network Architectures using Reinforcement LearningCode0
Q-Prop: Sample-Efficient Policy Gradient with An Off-Policy CriticCode0
A Differentiable Physics Engine for Deep Learning in Robotics—0
Combining policy gradient and Q-learning—0
Learning to Play in a Day: Faster Deep Reinforcement Learning by Optimality TighteningCode0
Using a Deep Reinforcement Learning Agent for Traffic Signal Control—0
Combating Reinforcement Learning's Sisyphean Curse with Intrinsic Fear—0
Internet of Things Applications: Animal Monitoring with Unmanned Aerial Vehicle—0
Active exploration in parameterized reinforcement learningCode0
Modelling Stock-market Investors as Reinforcement Learning Agents [Correction]—0
Playing FPS Games with Deep Reinforcement LearningCode0
Interactive Spoken Content Retrieval by Deep Reinforcement Learning—0
3D Simulation for Robot Arm Control with Deep Q-Learning—0
Episodic Exploration for Deep Deterministic Policies: An Application to StarCraft Micromanagement Tasks—0
Multi Exit Configuration of Mesoscopic Pedestrian Simulation—0
Q-Learning with Basic Emotions—0
BBQ-Networks: Efficient Exploration in Deep Reinforcement Learning for Task-Oriented Dialogue Systems—0
Learning to Communicate with Deep Multi-Agent Reinforcement LearningCode0
ViZDoom: A Doom-based AI Research Platform for Visual Reinforcement LearningCode0
Neurohex: A Deep Q-learning Hex Agent—0
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