VectorizedMultiAgentSimulator (VMAS) Files

VMAS is a vectorized differentiable simulator

This is an exact mirror of the VectorizedMultiAgentSimulator (VMAS) project, hosted at https://github.com/proroklab/VectorizedMultiAgentSimulator. SourceForge is not affiliated with VectorizedMultiAgentSimulator (VMAS). For more information, see the SourceForge Open Source Mirror Directory.

The interactive file manager requires Javascript. Please enable it or use sftp or scp.
You may still browse the files here.

Name	Modified	Size	InfoDownloads / Week
Parent folder
1.5.1 source code.tar.gz	2025-09-30	2.3 MB	0
1.5.1 source code.zip	2025-09-30	2.4 MB	0
README.md	2025-09-30	1.1 kB	0
Totals: 3 Items		4.7 MB	0

What's Changed

[Tutorial] New tutorial on creating a scenario and training it by @matteobettini in https://github.com/proroklab/VectorizedMultiAgentSimulator/pull/151
[Feature] Local seed by @matteobettini in https://github.com/proroklab/VectorizedMultiAgentSimulator/pull/154
[BugFix] Football u_shoot_multiplier by @matteobettini in https://github.com/proroklab/VectorizedMultiAgentSimulator/pull/156
fix bug of road_traffic senario when using cuda by @Schopenhauer-loves-Hegel in https://github.com/proroklab/VectorizedMultiAgentSimulator/pull/157
Ensure pos_shaping_factor is propagated in buzz_wire by @itwasabhi in https://github.com/proroklab/VectorizedMultiAgentSimulator/pull/160

New Contributors

@Schopenhauer-loves-Hegel made their first contribution in https://github.com/proroklab/VectorizedMultiAgentSimulator/pull/157
@itwasabhi made their first contribution in https://github.com/proroklab/VectorizedMultiAgentSimulator/pull/160

Full Changelog: https://github.com/proroklab/VectorizedMultiAgentSimulator/compare/1.5.0...1.5.1

Source: README.md, updated 2025-09-30

Other Useful Business Software

Auth0 for AI Agents now in GA Icon

Auth0 for AI Agents now in GA

Ready to implement AI with confidence (without sacrificing security)?

Connect your AI agents to apps and data more securely, give users control over the actions AI agents can perform and the data they can access, and enable human confirmation for critical agent actions.

Start building today

Turn traffic into pipeline and prospects into customers Icon

Turn traffic into pipeline and prospects into customers

For account executives and sales engineers looking for a solution to manage their insights and sales data

Docket is an AI-powered sales enablement platform designed to unify go-to-market (GTM) data through its proprietary Sales Knowledge Lake™ and activate it with intelligent AI agents. The platform helps marketing teams increase pipeline generation by 15% by engaging website visitors in human-like conversations and qualifying leads. For sales teams, Docket improves seller efficiency by 33% by providing instant product knowledge, retrieving collateral, and creating personalized documents. Built for GTM teams, Docket integrates with over 100 tools across the revenue tech stack and offers enterprise-grade security with SOC 2 Type II, GDPR, and ISO 27001 compliance. Customers report improved win rates, shorter sales cycles, and dramatically reduced response times. Docket’s scalable, accurate, and fast AI agents deliver reliable answers with confidence scores, empowering teams to close deals faster.

Learn More

Recommended Projects

AgentUniverse
agentUniverse is a LLM multi-agent framework
uAgents
A fast and lightweight framework for creating decentralized agents
OpenAI Agents SDK
A lightweight, powerful framework for multi-agent workflows
jason
Jason is a fully-fledged interpreter for an extended version of AgentSpeak, a BDI agent-oriented logic programming language, and is implemented in Java. Using JADE a multi-agent system can be distributed over a network effortlessly. This project was moved to https://jason-lang.github.io
PraisonAI
PraisonAI application combines AutoGen and CrewAI or similar framework