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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 726–750 of 1918 papers

TitleStatusHype
Exploration with Unreliable Intrinsic Reward in Multi-Agent Reinforcement Learning—0
Exploratory Control with Tsallis Entropy for Latent Factor Models—0
Exploring Competitive and Collusive Behaviors in Algorithmic Pricing with Deep Reinforcement Learning—0
Comparing NARS and Reinforcement Learning: An Analysis of ONA and Q-Learning Algorithms—0
Finite-Time Analysis of Whittle Index based Q-Learning for Restless Multi-Armed Bandits with Neural Network Function Approximation—0
Finite-Time Error Analysis of Online Model-Based Q-Learning with a Relaxed Sampling Model—0
Extrinsicaly Rewarded Soft Q Imitation Learning with Discriminator—0
Fair Loss: Margin-Aware Reinforcement Learning for Deep Face Recognition—0
Fast Adaptive Anti-Jamming Channel Access via Deep Q Learning and Coarse-Grained Spectrum Prediction—0
Fast Block Linear System Solver Using Q-Learning Schduling for Unified Dynamic Power System Simulations—0
Fast constraint satisfaction problem and learning-based algorithm for solving Minesweeper—0
GLSearch: Maximum Common Subgraph Detection via Learning to Search—0
Faster Deep Q-learning using Neural Episodic Control—0
Faster Non-asymptotic Convergence for Double Q-learning—0
Faster Q-Learning Algorithms for Restless Bandits—0
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
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