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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 751–800 of 1918 papers

TitleStatusHype
Logit-Q Dynamics for Efficient Learning in Stochastic Teams—0
Online Statistical Inference for Nonlinear Stochastic Approximation with Markovian Data—0
Deep Offline Reinforcement Learning for Real-world Treatment Optimization Applications—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
MACOptions: Multi-Agent Learning with Centralized Controller and Options Framework—0
Catch Me If You Can: Improving Adversaries in Cyber-Security With Q-Learning Algorithms—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
Analyzing Robustness of the Deep Reinforcement Learning Algorithm in Ramp Metering Applications Considering False Data Injection Attack and Defense—0
RCsearcher: Reaction Center Identification in Retrosynthesis via Deep Q-Learning—0
Single-Trajectory Distributionally Robust Reinforcement Learning—0
Learning from Multiple Independent Advisors in Multi-agent Reinforcement LearningCode0
FedHQL: Federated Heterogeneous Q-Learning—0
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
Temporal Difference Learning with Compressed Updates: Error-Feedback meets Reinforcement Learning—0
Contextual Conservative Q-Learning for Offline Reinforcement Learning—0
Deep Spectral Q-learning with Application to Mobile Health—0
NARS vs. Reinforcement learning: ONA vs. Q-LearningCode0
Decoding surface codes with deep reinforcement learning and probabilistic policy reuse—0
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
Reinforcement Learning for Resilient Power Grids—0
PALMER: Perception-Action Loop with Memory for Long-Horizon Planning—0
EASpace: Enhanced Action Space for Policy TransferCode0
A Machine with Short-Term, Episodic, and Semantic Memory SystemsCode0
Automata Learning meets ShieldingCode0
Welfare and Fairness in Multi-objective Reinforcement LearningCode0
Automatic Discovery of Multi-perspective Process Model using Reinforcement Learning—0
State-Aware Proximal Pessimistic Algorithms for Offline Reinforcement Learning—0
QLAMMP: A Q-Learning Agent for Optimizing Fees on Automated Market Making Protocols—0
Causal Deep Reinforcement Learning Using Observational Data—0
Offline Q-Learning on Diverse Multi-Task Data Both Scales And Generalizes—0
UAV-Assisted Space-Air-Ground Integrated Networks: A Technical Review of Recent Learning Algorithms—0
Explainable and Safe Reinforcement Learning for Autonomous Air MobilityCode0
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