Reinforcement learning for autonomous process control in industry 4.0: Advantages and challenges
N Nievas, A Pagès-Bernaus, F Bonada… - Applied Artificial …, 2024 - Taylor & Francis
In recent years, the integration of intelligent industrial process monitoring, quality prediction,
and predictive maintenance solutions has garnered significant attention, driven by rapid …
and predictive maintenance solutions has garnered significant attention, driven by rapid …
A Survey of Constraint Formulations in Safe Reinforcement Learning
Preparing for Black Swans: The Antifragility Imperative for Machine Learning
M ** - arxiv preprint arxiv:2405.11397, 2024 - arxiv.org
Operating safely and reliably despite continual distribution shifts is vital for high-stakes
machine learning applications. This paper builds upon the transformative concept …
machine learning applications. This paper builds upon the transformative concept …
SaVeR: Optimal Data Collection Strategy for Safe Policy Evaluation in Tabular MDP
In this paper, we study safe data collection for the purpose of policy evaluation in tabular
Markov decision processes (MDPs). In policy evaluation, we are given a\textit {target} policy …
Markov decision processes (MDPs). In policy evaluation, we are given a\textit {target} policy …
Safe Policy Optimization With Stretchable Penalties
In safe reinforcement learning (RL), ensuring cost-constraint satisfaction while minimizing
the sacrifice of reward acquisition for agent training presents a significant challenge. This …
the sacrifice of reward acquisition for agent training presents a significant challenge. This …
[PDF][PDF] Safe Resetless Reinforcement Learning: Enhancing Training Autonomy with Risk-Averse Agents
Training Reinforcement Learning agents directly in any realworld environment remains
difficult, as such scenarios entail the risk of damaging the training setup or violating other …
difficult, as such scenarios entail the risk of damaging the training setup or violating other …