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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 1841–1850 of 1918 papers

TitleStatusHype
Bridging the Gap Between Value and Policy Based Reinforcement Learning—0
Stabilising Experience Replay for Deep Multi-Agent Reinforcement LearningCode1
Reinforcement Learning with Deep Energy-Based PoliciesCode0
Learning Control for Air Hockey Striking using Deep Reinforcement Learning—0
Collaborative Deep Reinforcement Learning for Joint Object Search—0
The Game Imitation: Deep Supervised Convolutional Networks for Quick Video Game AI—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
Q-Prop: Sample-Efficient Policy Gradient with An Off-Policy CriticCode0
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