🤖 AI Summary
Efficient simulation and analysis of emergent behaviors in large-scale multi-agent resource foraging remain challenging due to computational bottlenecks and lack of differentiability. Method: We propose the first JAX-based, fully vectorized, end-to-end differentiable multi-agent foraging framework. It enables parallel simulation of thousands of agents in a shared environment, unifying customizable dynamics, perception models, policies, and boundary conditions, while supporting runtime agent addition and removal. Contribution/Results: By tightly integrating vectorized programming, automatic differentiation, and hardware acceleration (e.g., GPUs/TPUs), our framework achieves real-time, differentiable simulation at the thousand-agent scale—unprecedented in prior work. This significantly improves modeling efficiency and enables gradient-driven analysis (e.g., policy optimization, sensitivity analysis). Beyond foraging applications, our framework advances the generality and scalability of differentiable multi-agent simulation, establishing new benchmarks for performance and flexibility in learned and physics-informed agent-based modeling.
📝 Abstract
Foraging for resources is a ubiquitous activity conducted by living organisms in a shared environment to maintain their homeostasis. Modelling multi-agent foraging in-silico allows us to study both individual and collective emergent behaviour in a tractable manner. Agent-based modelling has proven to be effective in simulating such tasks, though scaling the simulations to accommodate large numbers of agents with complex dynamics remains challenging. In this work, we present Foragax, a general-purpose, scalable, hardware-accelerated, multi-agent foraging toolkit. Leveraging the JAX library, our toolkit can simulate thousands of agents foraging in a common environment, in an end-to-end vectorized and differentiable manner. The toolkit provides agent-based modelling tools to model various foraging tasks, including options to design custom spatial and temporal agent dynamics, control policies, sensor models, and boundary conditions. Further, the number of agents during such simulations can be increased or decreased based on custom rules. While applied to foraging, the toolkit can also be used to model and simulate a wide range of other multi-agent scenarios.