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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 1761–1770 of 1918 papers

TitleStatusHype
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
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