Topic: inter-agent influence

Social foraging is a fundamental collective behavior observed across various species, but studying complex multi-agent dynamics and accompanying neural activity in natural settings remains challenging. Here we present an in silico study of social interaction during foraging using recurrent neural network (RNN) agents trained using multi-agent reinforcement learning (MARL). Our framework explores how different group sizes, food availability, and sensory capabilities shape emergent foraging strategies in grops of 2 to 10 agents. These group sizes are especially relevant to experimental social ne