Parallelized Planning Acting for Efficient LLM based Multi Agent Systems скачать в хорошем качестве

Parallelized Planning Acting for Efficient LLM based Multi Agent Systems

Link to Arxiv Research Paper: https://arxiv.org/abs/2503.03505 Link to Colab Notebook: https://colab.research.google.com/dri... This video discusses a research paper on "Parallelized Planning Acting for Efficient LLM-based Multi-Agent Systems" [00:02]. Here's a breakdown of the key aspects: Framework Overview: The core concept involves a straightforward framework with separate planning and acting routes, operating concurrently with multiple planners and actors [00:17]. Minecraft Benchmark: The research utilizes Minecraft as a benchmark to evaluate the model's performance in various actions [01:16]. The parallel processing method significantly improves the model's ability to play Minecraft, achieving high accuracy in several areas [01:32]. Simulation and Architecture: The presenter built a complete simulation to understand the model's architecture, which includes a shared memory system for agents and channels [02:02]. The system updates memory with observations and action history, with individual planning and action threads operating simultaneously [02:22]. Recursive Task Decomposition: The model uses a skill library for recursive task decomposition, accessing skills like mining, crafting, and defending [03:18]. Centralized Memory System: A centralized memory system stores team-wide observations, chat logs, and action history, accessible to both planner and agent threads [04:14]. OpenAI Implementation: The video provides the necessary information to recreate the experiment using OpenAI APIs [05:05]. Swarm Algorithm Alternative: The presenter suggests that a swarm algorithm could be a more efficient approach, creating a class of action types (move, collect, craft, etc.) and utilizing pheromone-based deposits [05:35]. Parallel Processing: The swarm agents can execute actions in parallel, mirroring the framework's goal of simultaneous operations [06:41].