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Found 10 papers, 2 papers with code
Model-based Offline Quantum Reinforcement Learning
The policy is optimized with a gradient-free optimization scheme using the return estimate given by the model as the fitness function.
TinyMetaFed: Efficient Federated Meta-Learning for TinyML
The field of Tiny Machine Learning (TinyML) has made substantial advancements in democratizing machine learning on low-footprint devices, such as microcontrollers.
TinyReptile: TinyML with Federated Meta-Learning
Tiny machine learning (TinyML) is a rapidly growing field aiming to democratize machine learning (ML) for resource-constrained microcontrollers (MCUs).
SeLoC-ML: Semantic Low-Code Engineering for Machine Learning Applications in Industrial IoT
Therefore, this paper presents a framework called Semantic Low-Code Engineering for ML Applications (SeLoC-ML), built on a low-code platform to support the rapid development of ML applications in IIoT by leveraging Semantic Web technologies.
Interpretable Control by Reinforcement Learning
In this paper, three recently introduced reinforcement learning (RL) methods are used to generate human-interpretable policies for the cart-pole balancing benchmark.
Modeling System Dynamics with Physics-Informed Neural Networks Based on Lagrangian Mechanics
In this paper, we present physics-informed neural ordinary differential equations (PINODE), a hybrid model that combines the two modeling techniques to overcome the aforementioned problems.
Generating Interpretable Fuzzy Controllers using Particle Swarm Optimization and Genetic Programming
Autonomously training interpretable control strategies, called policies, using pre-existing plant trajectory data is of great interest in industrial applications.
Interpretable Policies for Reinforcement Learning by Genetic Programming
Here we introduce the genetic programming for reinforcement learning (GPRL) approach based on model-based batch reinforcement learning and genetic programming, which autonomously learns policy equations from pre-existing default state-action trajectory samples.
A Benchmark Environment Motivated by Industrial Control Problems
On one hand, these benchmarks are designed to provide interpretable RL training scenarios and detailed insight into the learning process of the method on hand.
Batch Reinforcement Learning on the Industrial Benchmark: First Experiences
The Particle Swarm Optimization Policy (PSO-P) has been recently introduced and proven to produce remarkable results on interacting with academic reinforcement learning benchmarks in an off-policy, batch-based setting.
