PommerLearn

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PommerLearn is an open-source reinforcement learning framework designed for the Pommerman environment. It provides modular implementations of PPO and DQN algorithms, environment wrappers, configurable training loops, built-in logging, model saving, and evaluation utilities to streamline agent development and research.
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May 11 2025
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PommerLearn

PommerLearn

0
0
PommerLearn
PommerLearn is an open-source reinforcement learning framework designed for the Pommerman environment. It provides modular implementations of PPO and DQN algorithms, environment wrappers, configurable training loops, built-in logging, model saving, and evaluation utilities to streamline agent development and research.
Added on:
Social & Email:
Platform:
May 11 2025
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Featured

What is PommerLearn?

PommerLearn enables researchers and developers to train multi-agent RL bots in the Pommerman game environment. It includes ready-to-use implementations of popular algorithms (PPO, DQN), flexible configuration files for hyperparameters, automatic logging and visualization of training metrics, model checkpointing, and evaluation scripts. Its modular architecture makes it easy to extend with new algorithms, customize environments, and integrate with standard ML libraries such as PyTorch.

Who will use PommerLearn?

  • Reinforcement learning researchers
  • Game AI developers
  • AI students and educators
  • Multi-agent systems enthusiasts

How to use the PommerLearn?

  • Step1: Clone the repository (git clone https://github.com/jw3il/PommerLearn).
  • Step2: Install dependencies (pip install -r requirements.txt).
  • Step3: Install Pommerman environment (follow Pommerman docs).
  • Step4: Configure training parameters in config files.
  • Step5: Run training script (python train.py --algo ppo).
  • Step6: Monitor logs and metrics via TensorBoard.
  • Step7: Evaluate saved models (python evaluate.py).

Platform

  • mac
  • windows
  • linux

PommerLearn's Core Features & Benefits

The Core Features

  • PPO algorithm implementation
  • DQN algorithm implementation
  • Pommerman environment wrappers
  • Configurable hyperparameters
  • Logging and TensorBoard integration
  • Model checkpointing and saving
  • Evaluation scripts

The Benefits

  • Fast setup for Pommerman RL experiments
  • Modular codebase for extension
  • Built-in logging and visualization
  • Supports multiple RL algorithms
  • Open-source and community-driven

PommerLearn's Main Use Cases & Applications

  • Training competitive Pommerman agents
  • Benchmarking PPO vs DQN in grid-world games
  • Educational RL tutorials and coursework
  • Research on multi-agent reinforcement learning

FAQs of PommerLearn

PommerLearn Company Information

PommerLearn Reviews

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

PommerLearn's Main Competitors and alternatives?

  • Pommerman Baseline Agents (official)
  • OpenAI Baselines
  • Stable-Baselines3
  • Ray RLlib

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