VectorizedMultiAgentSimulator
by proroklab
Vectorized differentiable 2D simulator for multi-agent RL benchmarking
How It Works
Provides a vectorized, differentiable 2D physics simulator for efficient multi-agent reinforcement learning benchmarking. Blackboard Pattern enhances modular coordination across scenarios, and the engine uses a PyTorch-based vectorized framework to run many parallel multi-robot scenarios with a modular scenario API. Model Context Protocol (MCP) Pattern aligns the simulator with gradient-based experiment needs. Optimized for speed and gradient-based experiments, making large-scale MARL sweeps and reproducible comparisons straightforward.
The Value Proposition
Target Use Cases
Researchers and engineers running large-scale MARL benchmarks who need fast, reproducible multi-robot scenarios and gradient-capable environments, leveraging the Dynamic Task Routing Pattern.
Use Cases
- Run large-scale parallel experiments to compare multi-agent policies and measure interaction failure modes
- Benchmark agent cooperation and competition using reproducible, vectorized multi-robot scenarios
- Use differentiable dynamics for research on gradient-based multi-agent learning and policy optimization