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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 1751–1775 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
Multi-Armed Bandits for Correlated Markovian Environments with Smoothed Reward Feedback—0
SA-IGA: A Multiagent Reinforcement Learning Method Towards Socially Optimal Outcomes—0
Smoothed Action Value Functions for Learning Gaussian Policies—0
Q-CP: Learning Action Values for Cooperative Planning—0
Deep Reinforcement Learning for Vision-Based Robotic Grasping: A Simulated Comparative Evaluation of Off-Policy MethodsCode0
Variance Reduction Methods for Sublinear Reinforcement Learning—0
Temporal Difference Models: Model-Free Deep RL for Model-Based Control—0
Weighted Double Deep Multiagent Reinforcement Learning in Stochastic Cooperative Environments—0
Efficient Collaborative Multi-Agent Deep Reinforcement Learning for Large-Scale Fleet ManagementCode0
A Deep Q-Learning Agent for the L-Game with Variable Batch TrainingCode0
Monte Carlo Q-learning for General Game PlayingCode0
Prioritized Sweeping Neural DynaQ with Multiple Predecessors, and Hippocampal Replays—0
Q-learning with Nearest Neighbors—0
M-Walk: Learning to Walk over Graphs using Monte Carlo Tree Search—0
Balancing Two-Player Stochastic Games with Soft Q-Learning—0
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