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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 1031–1040 of 1918 papers

TitleStatusHype
Federated Deep Q-Learning and 5G load balancing—0
Federated Double Deep Q-learning for Joint Delay and Energy Minimization in IoT networks—0
Federated Offline Reinforcement Learning: Collaborative Single-Policy Coverage Suffices—0
Federated Q-Learning: Linear Regret Speedup with Low Communication Cost—0
Federated Q-Learning with Reference-Advantage Decomposition: Almost Optimal Regret and Logarithmic Communication Cost—0
Federated Stochastic Approximation under Markov Noise and Heterogeneity: Applications in Reinforcement Learning—0
FedHQL: Federated Heterogeneous Q-Learning—0
Few is More: Task-Efficient Skill-Discovery for Multi-Task Offline Multi-Agent Reinforcement Learning—0
Fictitious play in zero-sum stochastic games—0
Fidelity-based Probabilistic Q-learning for Control of Quantum Systems—0
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