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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 1731–1740 of 1918 papers

TitleStatusHype
Multiagent Soft Q-Learning—0
Towards Symbolic Reinforcement Learning with Common SenseCode0
Benchmarking projective simulation in navigation problems—0
State Distribution-aware Sampling for Deep Q-learning—0
Nonparametric Stochastic Compositional Gradient Descent for Q-Learning in Continuous Markov Decision ProblemsCode0
Reinforced Co-Training—0
State-Augmentation Transformations for Risk-Sensitive Reinforcement Learning—0
CytonRL: an Efficient Reinforcement Learning Open-source Toolkit Implemented in C++Code0
Hierarchical Modular Reinforcement Learning Method and Knowledge Acquisition of State-Action Rule for Multi-target Problem—0
Information Maximizing Exploration with a Latent Dynamics Model—0
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