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Ant_racer

Ant_racer

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Ant_racer
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What is Ant_racer?

Ant_racer is a virtual multi-agent pursuit-evasion platform that provides a game environment for studying multi-agent reinforcement learning. Built on OpenAI Gym and Mujoco, it allows users to simulate interactions between multiple autonomous agents in pursuit and evasion tasks. The platform supports implementation and testing of reinforcement learning algorithms such as DDPG in a physically realistic environment. It is useful for researchers and developers interested in AI multi-agent behaviors in dynamic scenarios.

Who will use Ant_racer?

  • AI researchers
  • Machine learning practitioners
  • Robotics developers
  • Multi-agent systems researchers
  • Students studying reinforcement learning

How to use the Ant_racer?

  • Step1: Download and install Mujoco200 and its license in ~/.mujoco/
  • Step2: Set the environment variable LD_LIBRARY_PATH to Mujoco's bin directory
  • Step3: Create and activate the Anaconda virtual environment using ant_racer_env.yml
  • Step4: Clone the Ant_racer GitHub repository and navigate into it
  • Step5: Replace the gym folder in your environment with the one from the repository
  • Step6: Run the demo using 'python chase_demo.py' and troubleshoot any display errors

Platform

  • mac
  • windows
  • linux

Ant_racer's Core Features & Benefits

The Core Features

  • Autonomous goal decomposition and planning
  • Memory storage for context retention
  • Web browsing and data scraping
  • File system read/write operations
  • Recursive task execution and self-improvement

The Benefits

  • 24/7 unattended operation
  • Reduces manual task management
  • Accelerates research and content creation
  • Customizable workflows for diverse needs
  • Open-source transparency and extensibility

Ant_racer's Main Use Cases & Applications

  • Testing multi-agent pursuit-evasion algorithms
  • Research in reinforcement learning strategies
  • Benchmarking multi-agent systems
  • Studying autonomous agent interactions in dynamic environments

Ant_racer's Pros & Cons

The Pros

Open source and freely available
Built upon popular frameworks (Gym, Mujoco)
Provides demo and documented setup instructions
Suitable for academic research and experimentation

The Cons

Setup requires Mujoco installation which is proprietary
Limited platform support mainly desktop OS
No mobile or web platform versions
Documentation is minimal beyond basic setup

FAQs of Ant_racer

Ant_racer Company Information

Analytic of Ant_racer

Visit Over Time

Monthly Visits
479936.7k
Avg Visit Duration
00:06:28
Page Per Visit
6.09
Bounce Rate
36.14%
Apr 2025 - Jun 2025 All Traffic

Geography

Top 5 Regions
United States
18.86%
China
12.11%
India
9.81%
Russia
5.32%
Germany
3.83%
Apr 2025 - Jun 2025 Worldwide Desktop Only

Traffic Sources

Direct
53.23%
Search
32.40%
Referrals
12.04%
Social
2.13%
Paid Referrals
0.15%
Mail
0.05%
Apr 2025 - Jun 2025 Desktop Only

Top Keywords

KeywordTrafficCost Per Click
github6676.2k $ 1.27
zapret453.9k $ --
github copilot706.1k $ 1.19
запрет дискорд152.0k $ --
zapret-discord-youtube348.6k $ --

Ant_racer Reviews

5/5
Do You Recommend Ant_racer? Leave a Comment Below!

Ant_racer's Main Competitors and alternatives?

  • OpenAI Multi-Agent Particle Environment
  • PettingZoo multi-agent environments
  • Google Research Football
  • RLLib multi-agent capabilities

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