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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 751–775 of 1918 papers

TitleStatusHype
Forecasting and stabilizing chaotic regimes in two macroeconomic models via artificial intelligence technologies and control methods—0
Deep Offline Reinforcement Learning for Real-world Treatment Optimization Applications—0
Online Statistical Inference for Nonlinear Stochastic Approximation with Markovian Data—0
Computation Offloading for Uncertain Marine Tasks by Cooperation of UAVs and Vessels—0
A Lifetime Extended Energy Management Strategy for Fuel Cell Hybrid Electric Vehicles via Self-Learning Fuzzy Reinforcement Learning—0
Differentially Private Deep Q-Learning for Pattern Privacy Preservation in MEC Offloading—0
Catch Me If You Can: Improving Adversaries in Cyber-Security With Q-Learning Algorithms—0
MACOptions: Multi-Agent Learning with Centralized Controller and Options Framework—0
Offline Minimax Soft-Q-learning Under Realizability and Partial Coverage—0
Diversity Through Exclusion (DTE): Niche Identification for Reinforcement Learning through Value-Decomposition—0
Best Possible Q-Learning—0
Sample Complexity of Kernel-Based Q-Learning—0
The impact of surplus sharing on the outcomes of specific investments under negotiated transfer pricing: An agent-based simulation with fuzzy Q-learning agents—0
RCsearcher: Reaction Center Identification in Retrosynthesis via Deep Q-Learning—0
Analyzing Robustness of the Deep Reinforcement Learning Algorithm in Ramp Metering Applications Considering False Data Injection Attack and Defense—0
Single-Trajectory Distributionally Robust Reinforcement Learning—0
FedHQL: Federated Heterogeneous Q-Learning—0
Learning from Multiple Independent Advisors in Multi-agent Reinforcement LearningCode0
Asymptotic Convergence and Performance of Multi-Agent Q-Learning Dynamics—0
Asynchronous Deep Double Duelling Q-Learning for Trading-Signal Execution in Limit Order Book Markets—0
Risk-Averse Reinforcement Learning via Dynamic Time-Consistent Risk Measures—0
Decentralized model-free reinforcement learning in stochastic games with average-reward objective—0
Hierarchical Deep Q-Learning Based Handover in Wireless Networks with Dual Connectivity—0
Multi-Power Level Q-Learning Algorithm for Random Access in NOMA mMTC Systems—0
Tuning Path Tracking Controllers for Autonomous Cars Using Reinforcement Learning—0
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