Level Based Foraging

Level-based foraging research investigates how agents, both biological and artificial, optimize resource acquisition in spatially structured environments. Current research focuses on developing and analyzing algorithms, including reinforcement learning (particularly deep RL and its variants like PPO), neuro-evolution, and recurrent neural networks (like LSTMs), to model and improve foraging strategies, often within multi-agent cooperative settings. This work aims to understand optimal foraging behaviors, improve the efficiency of robotic systems in tasks like resource collection and warehouse management, and shed light on the underlying computational mechanisms of biological foraging. The insights gained have implications for both ecological modeling and the design of more efficient and robust autonomous systems.