MultiAgentPacman

MultiAgentPacman

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MultiAgentPacman is an educational Python framework that provides a ready-made Pacman environment for developing and testing various multi-agent AI algorithms. It includes built-in implementations of reflex agents, minimax, expectimax, alpha-beta pruning, and customizable agent templates, along with visualization and performance evaluation tools.
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May 18 2025
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MultiAgentPacman
MultiAgentPacman

MultiAgentPacman

0
0
MultiAgentPacman
MultiAgentPacman is an educational Python framework that provides a ready-made Pacman environment for developing and testing various multi-agent AI algorithms. It includes built-in implementations of reflex agents, minimax, expectimax, alpha-beta pruning, and customizable agent templates, along with visualization and performance evaluation tools.
Added on:
Social & Email:
Platform:
May 18 2025
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What is MultiAgentPacman?

MultiAgentPacman offers a Python-based game environment where users can implement, visualize, and benchmark multiple AI agents in the Pacman domain. It supports adversarial search algorithms like minimax, expectimax, alpha-beta pruning, as well as custom reinforcement learning or heuristic-based agents. The framework includes a simple GUI, command-line controls, and utilities to log game statistics and compare agent performance under competitive or cooperative scenarios.

Who will use MultiAgentPacman?

  • AI students and educators
  • Reinforcement learning researchers
  • Game AI developers
  • Academic instructors
  • AI algorithm enthusiasts

How to use the MultiAgentPacman?

  • Step1: Clone the repository via git clone https://github.com/TejasNaikk/MultiAgentPacman.git
  • Step2: Install Python 3.x and required dependencies (e.g., pygame) with pip install -r requirements.txt
  • Step3: Run sample games using python pacman.py -p ReflexAgent or python pacman.py -p MinimaxAgent
  • Step4: Create a custom agent by subclassing the Agent class in multiAgents.py
  • Step5: Launch your agent with python pacman.py -p YourAgentName -l mediumMaze -q
  • Step6: Use provided evaluation scripts to log wins, scores, and compare across multiple runs

Platform

  • Linux
  • Mac
  • Windows

MultiAgentPacman's Core Features & Benefits

The Core Features

  • Python-based Pacman game environment
  • Multiple built-in agents: Reflex, Minimax, Expectimax, Alpha-Beta
  • Custom agent API for heuristic and RL algorithms
  • Real-time GUI visualization
  • Command-line controls and logging utilities
  • Performance evaluation and statistics

The Benefits

  • Accelerates AI agent prototyping
  • Teaches core adversarial and multi-agent concepts
  • Facilitates reproducible benchmarking
  • Open-source and highly extensible
  • Interactive visualization of agent behaviors

MultiAgentPacman's Main Use Cases & Applications

  • University AI course assignments and labs
  • Benchmarking adversarial search algorithms
  • Testing multi-agent reinforcement learning strategies
  • Research in cooperative and competitive AI
  • Game AI prototyping and demonstrations

FAQs of MultiAgentPacman

MultiAgentPacman Company Information

MultiAgentPacman Reviews

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MultiAgentPacman's Main Competitors and alternatives?

Berkeley CS188 Pacman Projects
OpenAI Gym Pacman environments
PettingZoo multi-agent Pacman
PyGame Learning Environment (PLE)
gym-pacman

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