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.
AI Platforms & Frameworks
#reinforcement learning
#multi-agent system
#Pommerman game
#DQN algorithm
#PPO algorithm
#hyperparameter tuning
#training visualization
#model checkpointing
#open-source ML
#AI experiments
#ML libraries
#community-driven projects
#RL algorithms
#PommerLearn
#AI Development
#gaming AI
#bot training
#Performance Metrics
#automated logging
#configurable environments
...
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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.
AI Platforms & Frameworks
#reinforcement learning
#multi-agent system
#Pommerman game
#DQN algorithm
#PPO algorithm
#hyperparameter tuning
#training visualization
#model checkpointing
#open-source ML
#AI experiments
#ML libraries
#community-driven projects
#RL algorithms
#PommerLearn
#AI Development
#gaming AI
#bot training
#Performance Metrics
#automated logging
#configurable environments
...
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
- jw3il
PommerLearn Reviews
5/5
PommerLearn's Main Competitors and alternatives?
- Pommerman Baseline Agents (official)
- OpenAI Baselines
- Stable-Baselines3
- Ray RLlib
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