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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 1851–1860 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
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