Towards a Domain-Specific Modelling Environment for Reinforcement Learning
- URL: http://arxiv.org/abs/2410.09368v1
- Date: Sat, 12 Oct 2024 04:56:01 GMT
- Title: Towards a Domain-Specific Modelling Environment for Reinforcement Learning
- Authors: Natalie Sinani, Sahil Salma, Paul Boutot, Sadaf Mustafiz,
- Abstract summary: We use model-driven engineering (MDE) methods and tools for developing a domain-specific modelling environment.
We targeted reinforcement learning from the machine learning domain, and evaluated the proposed language, reinforcement learning modelling language (RLML)
The tool supports syntax-directed editing, constraint checking, and automatic generation of code from RLML models.
- Score: 0.13124513975412253
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: In recent years, machine learning technologies have gained immense popularity and are being used in a wide range of domains. However, due to the complexity associated with machine learning algorithms, it is a challenge to make it user-friendly, easy to understand and apply. Machine learning applications are especially challenging for users who do not have proficiency in this area. In this paper, we use model-driven engineering (MDE) methods and tools for developing a domain-specific modelling environment to contribute towards providing a solution for this problem. We targeted reinforcement learning from the machine learning domain, and evaluated the proposed language, reinforcement learning modelling language (RLML), with multiple applications. The tool supports syntax-directed editing, constraint checking, and automatic generation of code from RLML models. The environment also provides support for comparing results generated with multiple RL algorithms. With our proposed MDE approach, we were able to help in abstracting reinforcement learning technologies and improve the learning curve for RL users.
Related papers
- Adaptive Tool Use in Large Language Models with Meta-Cognition Trigger [49.81945268343162]
We propose MeCo, an adaptive decision-making strategy for external tool use.
MeCo captures high-level cognitive signals in the representation space, guiding when to invoke tools.
Our experiments show that MeCo accurately detects LLMs' internal cognitive signals and significantly improves tool-use decision-making.
arXiv Detail & Related papers (2025-02-18T15:45:01Z) - Darkit: A User-Friendly Software Toolkit for Spiking Large Language Model [50.37090759139591]
Large language models (LLMs) have been widely applied in various practical applications, typically comprising billions of parameters.
The human brain, employing bio-plausible spiking mechanisms, can accomplish the same tasks while significantly reducing energy consumption.
We are releasing a software toolkit named DarwinKit (Darkit) to accelerate the adoption of brain-inspired large language models.
arXiv Detail & Related papers (2024-12-20T07:50:08Z) - AdaptoML-UX: An Adaptive User-centered GUI-based AutoML Toolkit for Non-AI Experts and HCI Researchers [19.602247178319992]
We introduce AdaptoML-UX, an adaptive framework that incorporates automated feature engineering, machine learning, and incremental learning.
Our toolkit demonstrates the capability to adapt efficiently to diverse problem domains and datasets.
It supports model personalization through incremental learning, customizing models to individual user behaviors.
arXiv Detail & Related papers (2024-10-22T22:52:14Z) - LVLM-Interpret: An Interpretability Tool for Large Vision-Language Models [50.259006481656094]
We present a novel interactive application aimed towards understanding the internal mechanisms of large vision-language models.
Our interface is designed to enhance the interpretability of the image patches, which are instrumental in generating an answer.
We present a case study of how our application can aid in understanding failure mechanisms in a popular large multi-modal model: LLaVA.
arXiv Detail & Related papers (2024-04-03T23:57:34Z) - Machine Learning Insides OptVerse AI Solver: Design Principles and
Applications [74.67495900436728]
We present a comprehensive study on the integration of machine learning (ML) techniques into Huawei Cloud's OptVerse AI solver.
We showcase our methods for generating complex SAT and MILP instances utilizing generative models that mirror multifaceted structures of real-world problem.
We detail the incorporation of state-of-the-art parameter tuning algorithms which markedly elevate solver performance.
arXiv Detail & Related papers (2024-01-11T15:02:15Z) - AutoML-GPT: Large Language Model for AutoML [5.9145212342776805]
We have established a framework called AutoML-GPT that integrates a comprehensive set of tools and libraries.
Through a conversational interface, users can specify their requirements, constraints, and evaluation metrics.
We have demonstrated that AutoML-GPT significantly reduces the time and effort required for machine learning tasks.
arXiv Detail & Related papers (2023-09-03T09:39:49Z) - Learning Environment Models with Continuous Stochastic Dynamics [0.0]
We aim to provide insights into the decisions faced by the agent by learning an automaton model of environmental behavior under the control of an agent.
In this work, we raise the capabilities of automata learning such that it is possible to learn models for environments that have complex and continuous dynamics.
We apply our automata learning framework on popular RL benchmarking environments in the OpenAI Gym, including LunarLander, CartPole, Mountain Car, and Acrobot.
arXiv Detail & Related papers (2023-06-29T12:47:28Z) - Panoramic Learning with A Standardized Machine Learning Formalism [116.34627789412102]
This paper presents a standardized equation of the learning objective, that offers a unifying understanding of diverse ML algorithms.
It also provides guidance for mechanic design of new ML solutions, and serves as a promising vehicle towards panoramic learning with all experiences.
arXiv Detail & Related papers (2021-08-17T17:44:38Z) - Resource-Aware Pareto-Optimal Automated Machine Learning Platform [1.6746303554275583]
novel platform Resource-Aware AutoML (RA-AutoML)
RA-AutoML enables flexible and generalized algorithms to build machine learning models subjected to multiple objectives.
arXiv Detail & Related papers (2020-10-30T19:37:48Z) - Conditional Generative Modeling via Learning the Latent Space [54.620761775441046]
We propose a novel framework for conditional generation in multimodal spaces.
It uses latent variables to model generalizable learning patterns.
At inference, the latent variables are optimized to find optimal solutions corresponding to multiple output modes.
arXiv Detail & Related papers (2020-10-07T03:11:34Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.