MADDPG-Keras is an open-source Python library implementing the Multi-Agent Deep Deterministic Policy Gradient algorithm using Keras and TensorFlow. It provides configurable environments, training scripts, and utilities to develop and evaluate cooperative and competitive multi-agent reinforcement learning scenarios.
AI Platforms & Frameworks
#multi-agent reinforcement learning
#MADDPG algorithm
#Keras framework
#continuous action spaces
#OpenAI Gym environments
#neural network customization
#experiment tracking
#GPU Acceleration
#RL research
#logging features
#hyperparameter tuning
#model checkpointing
#TensorBoard-Integration
#open-source AI tools
#policy learning
#Benchmarking
#machine learning experiments
#configurable codebase
#research community
#AI experiment setup
...
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MADDPG-Keras
MADDPG-Keras is an open-source Python library implementing the Multi-Agent Deep Deterministic Policy Gradient algorithm using Keras and TensorFlow. It provides configurable environments, training scripts, and utilities to develop and evaluate cooperative and competitive multi-agent reinforcement learning scenarios.
AI Platforms & Frameworks
#multi-agent reinforcement learning
#MADDPG algorithm
#Keras framework
#continuous action spaces
#OpenAI Gym environments
#neural network customization
#experiment tracking
#GPU Acceleration
#RL research
#logging features
#hyperparameter tuning
#model checkpointing
#TensorBoard-Integration
#open-source AI tools
#policy learning
#Benchmarking
#machine learning experiments
#configurable codebase
#research community
#AI experiment setup
...
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What is MADDPG-Keras?
MADDPG-Keras delivers a complete framework for multi-agent reinforcement learning research by implementing the MADDPG algorithm in Keras. It supports continuous action spaces, multiple agents, and standard OpenAI Gym environments. Researchers and developers can configure neural network architectures, training hyperparameters, and reward functions, then launch experiments with built-in logging and model checkpointing to accelerate multi-agent policy learning and benchmarking.
Who will use MADDPG-Keras?
- Reinforcement learning researchers
- Machine learning engineers
- AI students and educators
- Multi-agent systems developers
How to use the MADDPG-Keras?
- Step1: Clone the repository from GitHub: git clone https://github.com/pr-shukla/maddpg-keras.git
- Step2: Navigate to the project folder: cd maddpg-keras
- Step3: Install dependencies: pip install -r requirements.txt
- Step4: Configure environment and hyperparameters in config.py or via command-line args
- Step5: Run training script: python main.py --env
--num_agents - Step6: Monitor training output, view logs and checkpoints in the logs/ directory
Platform
- mac
- windows
- linux
MADDPG-Keras's Core Features & Benefits
The Core Features
- Keras & TensorFlow implementation of MADDPG
- Support for continuous action spaces
- Configurable multi-agent Gym environments
- Logging, tensorboard integration, and checkpointing
- Customizable neural network architectures
The Benefits
- Accelerates multi-agent RL experiment setup
- Easily extendable and configurable codebase
- Built-in experiment tracking and visualization
- Leverages GPU acceleration for faster training
- Open-source and community-driven
MADDPG-Keras's Main Use Cases & Applications
- Cooperative navigation among multiple agents
- Predator-prey pursuit and evasion scenarios
- Resource allocation in distributed systems
- Autonomous vehicle coordination
- Multi-robot task assignment and cooperation
FAQs of MADDPG-Keras
MADDPG-Keras Company Information
- pr-shukla
MADDPG-Keras Reviews
5/5
MADDPG-Keras's Main Competitors and alternatives?
- OpenAI Multi-Agent Particle Environment
- Ray RLlib
- PyMARL
- MAVA
- Stable Baselines3
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