Level-Based Foraging

Level-Based Foraging is a benchmark used to evaluate multi-agent reinforcement learning algorithms in environments where agents can dynamically spawn, focusing on their ability to adapt to varying population sizes and environmental demands.

Papers using Level-Based Foraging (7)

Multi-Agent Model-Based Reinforcement Learning with Joint State-Action Learned Embeddings2026Fluid-Agent Reinforcement Learning2026Policy-Conditioned Policies for Multi-Agent Task Solving2025Fault Tolerant Multi-Agent Learning with Adversarial Budget Constraints2025Concept Learning for Cooperative Multi-Agent Reinforcement Learning2025JaxRobotarium: Training and Deploying Multi-Robot Policies in 10 Minutes2025Dynamic Sight Range Selection in Multi-Agent Reinforcement Learning2025