CybMASDE is an open-source Python framework designed to simulate, train, and evaluate cooperative multi-agent deep reinforcement learning scenarios. It offers customizable environments, reward structures, and integration with popular RL libraries like PyTorch and TensorFlow. With built-in visualization, logging, and metric tracking tools, users can design complex agent interactions, benchmark novel algorithms, and iterate rapidly on multi-agent system research and development projects.
AI Platforms & Frameworks
Development Environment
#multi-agent simulation
#deep reinforcement learning
#custom scenarios
#agent roles
#reward functions
#parallel training
#real-time monitoring
#model checkpointing
#swarm intelligence
#resource allocation
#experiment tracking
#Open Source Framework
#PyTorch Integration
#TensorFlow integration
#networked agents
#Data Visualization
#scalable solutions
#RL algorithms
#cooperative control
#modular architecture
...
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CybMASDE
CybMASDE is an open-source Python framework designed to simulate, train, and evaluate cooperative multi-agent deep reinforcement learning scenarios. It offers customizable environments, reward structures, and integration with popular RL libraries like PyTorch and TensorFlow. With built-in visualization, logging, and metric tracking tools, users can design complex agent interactions, benchmark novel algorithms, and iterate rapidly on multi-agent system research and development projects.
AI Platforms & Frameworks
Development Environment
#multi-agent simulation
#deep reinforcement learning
#custom scenarios
#agent roles
#reward functions
#parallel training
#real-time monitoring
#model checkpointing
#swarm intelligence
#resource allocation
#experiment tracking
#Open Source Framework
#PyTorch Integration
#TensorFlow integration
#networked agents
#Data Visualization
#scalable solutions
#RL algorithms
#cooperative control
#modular architecture
...
Featured
What is CybMASDE?
CybMASDE enables researchers and developers to build, configure, and execute multi-agent simulations with deep reinforcement learning. Users can author custom scenarios, define agent roles and reward functions, and plug in standard or custom RL algorithms. The framework includes environment servers, networked agent interfaces, data collectors, and rendering utilities. It supports parallel training, real-time monitoring, and model checkpointing. CybMASDE’s modular architecture allows seamless integration of new agents, observation spaces, and training strategies, accelerating experimentation in cooperative control, swarm behavior, resource allocation, and other multi-agent use cases.
Who will use CybMASDE?
- AI researchers in reinforcement learning
- Multi-agent systems developers
- Robotics engineers
- Academic instructors and students
- AI-focused data scientists
How to use the CybMASDE?
- Step1: Install via pip install cybmasde
- Step2: Import CybMASDE and configure Python environment
- Step3: Define agent classes, observation and action spaces
- Step4: Create or select a built-in environment scenario
- Step5: Choose or integrate a deep RL algorithm (e.g., PPO, DDPG)
- Step6: Configure training parameters and reward functions
- Step7: Launch training with parallel or single-process mode
- Step8: Monitor progress using built-in logs and visualizers
- Step9: Evaluate trained policies and adjust scenario settings
- Step10: Export and deploy agent models for further testing
Platform
- mac
- windows
- linux
CybMASDE's Core Features & Benefits
The Core Features
- Customizable multi-agent environment scenarios
- Integration with PyTorch and TensorFlow
- Parallel training and distributed execution
- Built-in visualization and logging tools
- Modular reward and observation configuration
- Checkpointing and metric tracking
The Benefits
- Accelerates multi-agent RL research
- Flexible scenario authoring and extension
- Scalable training across CPUs and GPUs
- Comprehensive experiment tracking
- Open-source and community-driven
- Easy integration with existing RL libraries
CybMASDE's Main Use Cases & Applications
- Swarm robotics coordination research
- Resource allocation in networked systems
- Cooperative game AI development
- Academic teaching of multi-agent reinforcement learning
- Benchmarking novel MARL algorithms
FAQs of CybMASDE
CybMASDE Company Information
- julien6
CybMASDE Reviews
5/5
CybMASDE's Main Competitors and alternatives?
- PettingZoo
- OpenAI Gym with multi-agent extensions
- RLlib
- MAgent
- Multi-Agent Particle Environment
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