Cross Language Soccer Framework: An Open Source Framework for the RoboCup 2D Soccer Simulation

• URL: http://arxiv.org/abs/2406.05621v1
• Date: Sun, 9 Jun 2024 03:11:40 GMT
• Title: Cross Language Soccer Framework: An Open Source Framework for the RoboCup 2D Soccer Simulation
• Authors: Nader Zare, Aref Sayareh, Alireza Sadraii, Arad Firouzkouhi, Amilcar Soares,
• Abstract summary: RoboCup Soccer Simulation 2D (SS2D) research is hampered by the complexity of existing Cpp-based codes like Helios, Cyrus, and Gliders. This development paper introduces a transformative solution a g-based, language-agnostic framework that seamlessly integrates with the high-performance Helios base code.
• Score: 0.4660328753262075
• License: http://creativecommons.org/publicdomain/zero/1.0/
• Abstract: RoboCup Soccer Simulation 2D (SS2D) research is hampered by the complexity of existing Cpp-based codes like Helios, Cyrus, and Gliders, which also suffer from limited integration with modern machine learning frameworks. This development paper introduces a transformative solution a gRPC-based, language-agnostic framework that seamlessly integrates with the high-performance Helios base code. This approach not only facilitates the use of diverse programming languages including CSharp, JavaScript, and Python but also maintains the computational efficiency critical for real time decision making in SS2D. By breaking down language barriers, our framework significantly enhances collaborative potential and flexibility, empowering researchers to innovate without the overhead of mastering or developing extensive base codes. We invite the global research community to leverage and contribute to the Cross Language Soccer (CLS) framework, which is openly available under the MIT License, to drive forward the capabilities of multi-agent systems in soccer simulations.

Related papers

• Syntactic and Semantic Control of Large Language Models via Sequential Monte Carlo [90.78001821963008]
  A wide range of LM applications require generating text that conforms to syntactic or semantic constraints. We develop an architecture for controlled LM generation based on sequential Monte Carlo (SMC) Our system builds on the framework of Lew et al. (2023) and integrates with its language model probabilistic programming language.
  arXiv  Detail & Related papers  (2025-04-17T17:49:40Z)
• Multi-Programming Language Sandbox for LLMs [78.99934332554963]
  out-of-the-box multi-programming language sandbox designed to provide unified and comprehensive feedback from compiler and analysis tools for Large Language Models (LLMs) It can automatically identify the programming language of the code, compiling and executing it within an isolated sub-sandbox to ensure safety and stability.
  arXiv  Detail & Related papers  (2024-10-30T14:46:43Z)
• Contextual Augmented Multi-Model Programming (CAMP): A Hybrid Local-Cloud Copilot Framework [8.28588489551341]
  This paper presents CAMP, a multi-model AI-assisted programming framework that consists of a local model that employs Retrieval-Augmented Generation (RAG) RAG retrieves contextual information from the cloud model to facilitate context-aware prompt construction. The methodology is actualized in Copilot for Xcode, an AI-assisted programming tool crafted for the Apple software ecosystem.
  arXiv  Detail & Related papers  (2024-10-20T04:51:24Z)
• Multi-Programming Language Ensemble for Code Generation in Large Language Model [5.882816711878273]
  Large language models (LLMs) have significantly improved code generation, particularly in one-pass code generation. Most existing approaches focus solely on generating code in a single programming language, overlooking the potential of leveraging the multi-language capabilities of LLMs. We propose Multi-Programming Language Ensemble (MPLE), a novel ensemble-based method that utilizes code generation across multiple programming languages to enhance overall performance.
  arXiv  Detail & Related papers  (2024-09-06T08:31:18Z)
• Multi-Agent Software Development through Cross-Team Collaboration [30.88149502999973]
  We introduce Cross-Team Collaboration (CTC), a scalable multi-team framework for software development. CTC enables orchestrated teams to jointly propose various decisions and communicate with their insights. Results show a notable increase in quality compared to state-of-the-art baselines.
  arXiv  Detail & Related papers  (2024-06-13T10:18:36Z)
• A Framework to Model ML Engineering Processes [1.9744907811058787]
  Development of Machine Learning (ML) based systems is complex and requires multidisciplinary teams with diverse skill sets. Current process modeling languages are not suitable for describing the development of such systems. We introduce a framework for modeling ML-based software development processes, built around a domain-specific language.
  arXiv  Detail & Related papers  (2024-04-29T09:17:36Z)
• LMRL Gym: Benchmarks for Multi-Turn Reinforcement Learning with Language Models [56.25156596019168]
  This paper introduces the LMRL-Gym benchmark for evaluating multi-turn RL for large language models (LLMs) Our benchmark consists of 8 different language tasks, which require multiple rounds of language interaction and cover a range of tasks in open-ended dialogue and text games.
  arXiv  Detail & Related papers  (2023-11-30T03:59:31Z)
• MAgIC: Investigation of Large Language Model Powered Multi-Agent in Cognition, Adaptability, Rationality and Collaboration [102.41118020705876]
  Large Language Models (LLMs) have marked a significant advancement in the field of natural language processing. As their applications extend into multi-agent environments, a need has arisen for a comprehensive evaluation framework. This work introduces a novel benchmarking framework specifically tailored to assess LLMs within multi-agent settings.
  arXiv  Detail & Related papers  (2023-11-14T21:46:27Z)
• GameGPT: Multi-agent Collaborative Framework for Game Development [3.5100485879548127]
  Large language model (LLM) based agents have demonstrated their capacity to automate and expedite software development processes. We propose a multi-agent collaborative framework, dubbed GameGPT, to automate game development.
  arXiv  Detail & Related papers  (2023-10-12T06:31:43Z)
• ChatDev: Communicative Agents for Software Development [84.90400377131962]
  ChatDev is a chat-powered software development framework in which specialized agents are guided in what to communicate. These agents actively contribute to the design, coding, and testing phases through unified language-based communication.
  arXiv  Detail & Related papers  (2023-07-16T02:11:34Z)
• InterCode: Standardizing and Benchmarking Interactive Coding with Execution Feedback [50.725076393314964]
  We introduce InterCode, a lightweight, flexible, and easy-to-use framework of interactive coding as a standard reinforcement learning environment. Our framework is language and platform agnostic, uses self-contained Docker environments to provide safe and reproducible execution. We demonstrate InterCode's viability as a testbed by evaluating multiple state-of-the-art LLMs configured with different prompting strategies.
  arXiv  Detail & Related papers  (2023-06-26T17:59:50Z)
• CodeTF: One-stop Transformer Library for State-of-the-art Code LLM [72.1638273937025]
  We present CodeTF, an open-source Transformer-based library for state-of-the-art Code LLMs and code intelligence. Our library supports a collection of pretrained Code LLM models and popular code benchmarks. We hope CodeTF is able to bridge the gap between machine learning/generative AI and software engineering.
  arXiv  Detail & Related papers  (2023-05-31T05:24:48Z)

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.