Q-learning
PulseAugur coverage of Q-learning — every cluster mentioning Q-learning across labs, papers, and developer communities, ranked by signal.
9 day(s) with sentiment data
-
New RL algorithm optimizes laser cutting parameters, reducing time and waste
A new research paper introduces the Reinforcement Learning for Laser Cutting (RL^2C) algorithm, designed to optimize parameters for laser-based cutting of optical films. This Q-learning based approach significantly redu…
-
New framework enables online statistical inference for complex AI algorithms
Researchers have developed a novel online statistical inference framework for nonlinear stochastic approximation algorithms that utilize Markovian data. This framework establishes a functional central limit theorem for …
-
New LUQ-Learning method optimizes patient-specific treatment regimes
Researchers have developed Latent Utility Q-Learning (LUQ-Learning), a novel method for optimizing dynamic treatment regimes (DTRs) that accounts for patients' differing preferences across multiple outcomes. This approa…
-
New adaptive training controller enhances risk-aware Q-learning for financial tasks
Researchers have developed an adaptive training controller for Conditional Value-at-Risk (CVaR) risk-aware Q-learning (RaQL) to improve its stability and sample efficiency in financial applications. This controller intr…
-
New SADQ method enhances Q-learning stability in Deep Q-Networks
Researchers have introduced the Successor Rollout Aggregation Deep Q-Network (SADQ), a novel modification to Q-learning designed to improve training stability in Deep Q-Networks (DQNs). SADQ addresses the issue of DQNs …
-
Gated Q-learning offers new approach to reinforcement learning bias
Researchers have introduced Gated Q-learning, a new framework designed to address the long-standing challenge of balancing off-policy bias and sample efficiency in reinforcement learning. Unlike previous methods that fo…
-
TRUAV framework uses distributed Q-learning for UAV-aided VANETs
Researchers have developed TRUAV, a novel distributed multi-agent reinforcement learning framework designed for trajectory planning and routing enhancement in UAV-aided vehicular ad hoc networks (VANETs). This system ut…
-
New metrics reveal temporal fairness gaps in multi-agent AI coordination
Researchers have developed new metrics to evaluate temporal fairness in multi-agent systems, particularly in repeated game scenarios. These "Alternation (ALT) metrics" address the limitations of traditional outcome-base…
-
Equilibrium stability drives cooperation in Q-learning algorithms
A new research paper explores how equilibrium stability can drive cooperation among Q-learners, particularly in scenarios where exploration does not vanish over time. The study focuses on the repeated Prisoner's Dilemma…
-
ADORN uses reinforcement learning to manage AI/ML model drift in Open RAN
Researchers have developed ADORN, a novel approach to manage performance drift in AI/ML models used in Open Radio Access Networks (O-RAN). The system utilizes a Q-learning-based reinforcement learning agent to make adap…
-
Researchers provide spectral analysis and convergence guarantees for dueling Q-learning
This paper presents a spectral analysis of dueling Q-learning, an extension of the Q-learning algorithm used in reinforcement learning. The research focuses on providing theoretical understanding and convergence guarant…
-
Reinforcement Learning series covers Q-learning and its impact on DQN
Shawn Hymel has published the tenth installment of his Reinforcement Learning series, focusing on Q-learning. This method differs from SARSA by utilizing the maximum Q-estimate for the next action, a technique that pave…
-
Causal reasoning in RL faces challenges with corrupted data, article finds
A new article explores the challenges of integrating causal reasoning into reinforcement learning (RL) agents. While causal models promise enhanced generalization and intervention capabilities for RL, they can also lead…
-
Q-learning theory advanced with new error analysis and switching system framework · 2 sources tracked
Two new research papers analyze Q-learning, a fundamental reinforcement learning algorithm, from different theoretical perspectives. The first paper focuses on the overestimation bias inherent in Q-learning, decomposing…
-
New RL-HGGA algorithm speeds up bin packing problem solutions
Researchers have developed RL-HGGA, a novel algorithm that combines a metaheuristic approach with reinforcement learning to solve the one-dimensional bin packing problem. This hybrid method uses a Q-learning agent to dy…
-
New DiPS framework enhances LLM persuasion in high-stakes scenarios
Researchers have developed DiPS, a Q-learning framework designed to improve persuasion capabilities in large language models (LLMs) for high-stakes situations. This system dynamically selects persuasion strategies based…
-
New mean-expansion layer accelerates reinforcement learning value sharing
Researchers have developed a new method called the mean-expansion layer to accelerate the learning of action-values in reinforcement learning algorithms like Q-learning. This layer efficiently shares value information a…
-
New Heavy-Ball Q-Learning method promises faster reinforcement learning convergence
Researchers have introduced a novel Heavy-Ball Q-Learning method designed to enhance reinforcement learning algorithms. This new approach establishes convergence guarantees and identifies conditions under which it can t…
-
New robust Q-learning algorithm tackles mean-field control with Wasserstein uncertainty
Researchers have developed a new robust Q-learning algorithm designed for mean-field control problems. This algorithm addresses challenges posed by Wasserstein uncertainty in common noise laws by integrating a quantizat…
-
Quantum Annealing boosts AI for predictive maintenance · 2 sources tracked
Researchers have developed a novel Quantum Annealing enhanced Q-Learning (QAQL) framework to improve Remaining Useful Lifetime (RUL) prediction in predictive maintenance. This approach integrates quantum annealing's sam…