SOTAVerified

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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 741–750 of 1918 papers

TitleStatusHype
Fastest Convergence for Q-learning—0
Fast-Fading Channel and Power Optimization of the Magnetic Inductive Cellular Network—0
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
Fire Threat Detection From Videos with Q-Rough Sets—0
Show:102550
← PrevPage 75 of 192Next →

No leaderboard results yet.