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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 981–990 of 1918 papers

TitleStatusHype
Estimation Error Correction in Deep Reinforcement Learning for Deterministic Actor-Critic MethodsCode0
MEPG: A Minimalist Ensemble Policy Gradient Framework for Deep Reinforcement Learning—0
Off-line approximate dynamic programming for the vehicle routing problem with a highly variable customer basis and stochastic demands—0
Search For Deep Graph Neural Networks—0
Greedy UnMixing for Q-Learning in Multi-Agent Reinforcement Learning—0
Regularize! Don't Mix: Multi-Agent Reinforcement Learning without Explicit Centralized Structures—0
Learning from Peers: Deep Transfer Reinforcement Learning for Joint Radio and Cache Resource Allocation in 5G RAN Slicing—0
Convergence of a Human-in-the-Loop Policy-Gradient Algorithm With Eligibility Trace Under Reward, Policy, and Advantage Feedback—0
Optimal Cycling of a Heterogenous Battery Bank via Reinforcement Learning—0
Deep hierarchical reinforcement agents for automated penetration testing—0
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