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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 1821–1830 of 1918 papers

TitleStatusHype
Generalized Value Iteration Networks: Life Beyond LatticesCode0
Multi-Agent Actor-Critic for Mixed Cooperative-Competitive EnvironmentsCode1
UCB Exploration via Q-Ensembles—0
Implications of Decentralized Q-learning Resource Allocation in Wireless NetworksCode0
Learning to Factor Policies and Action-Value Functions: Factored Action Space Representations for Deep Reinforcement learning—0
A Comparison of Reinforcement Learning Techniques for Fuzzy Cloud Auto-Scaling—0
Identification and Off-Policy Learning of Multiple Objectives Using Adaptive Clustering—0
Learning to Represent Haptic Feedback for Partially-Observable Tasks—0
Learning Hard Alignments with Variational Inference—0
Discrete Sequential Prediction of Continuous Actions for Deep RL—0
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