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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 1751–1760 of 1918 papers

TitleStatusHype
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
Joint Learning of Interactive Spoken Content Retrieval and Trainable User Simulator—0
Deep Reinforcement Learning for Traffic Light Control in Vehicular NetworksCode0
Natural Gradient Deep Q-learning—0
Composable Deep Reinforcement Learning for Robotic ManipulationCode0
Learning to Explore with Meta-Policy Gradient—0
Deep reinforcement learning for time series: playing idealized trading gamesCode0
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