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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 781–790 of 1918 papers

TitleStatusHype
Control of Continuous Quantum Systems with Many Degrees of Freedom based on Convergent Reinforcement LearningCode0
Bandit approach to conflict-free multi-agent Q-learning in view of photonic implementation—0
Taming Lagrangian Chaos with Multi-Objective Reinforcement Learning—0
Offline Robot Reinforcement Learning with Uncertainty-Guided Human Expert Sampling—0
Distributed-Training-and-Execution Multi-Agent Reinforcement Learning for Power Control in HetNetCode0
VOQL: Towards Optimal Regret in Model-free RL with Nonlinear Function Approximation—0
Frugal Reinforcement-based Active Learning—0
PALMER: Perception-Action Loop with Memory for Long-Horizon Planning—0
Reinforcement Learning for Resilient Power Grids—0
EASpace: Enhanced Action Space for Policy TransferCode0
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