Search Results for "multi agent simulation"

LatentMAS is an advanced framework for multi-agent reinforcement learning (MARL) that uses latent variable modeling to bridge perception and decision-making in environments where agents must coordinate under uncertainty. It provides mechanisms for agents to learn high-level latent representations of states, which simplifies complex sensory inputs into compact, actionable embeddings that facilitate both individual policy learning and inter-agent coordination.

TREK is a system designed to explore task execution, reasoning, or knowledge traversal in structured environments, likely focusing on guiding agents through complex multi-step processes. The project appears to emphasize traceability, allowing users to follow how decisions are made and how tasks progress through different stages. Its architecture suggests a focus on modular reasoning, where each step in a process is clearly defined and can be analyzed independently. This makes it particularly useful for debugging, experimentation, and understanding agent behavior in complex workflows. ...