[AAAI 2025 Oral] Synergistic Multi-Agent Framework with Trajectory Learning for Knowledge-Intensive Tasks
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Updated
Apr 14, 2025 - Python
[AAAI 2025 Oral] Synergistic Multi-Agent Framework with Trajectory Learning for Knowledge-Intensive Tasks
Clean, documented implementations of PPO-based algorithms for cooperative multi-agent reinforcement learning, focusing on SMAC environments. Features MLP and RNN-based MAPPO and HAPPO with various techniques.
SUBSUMPTION PATTERN LEARNING (SPL) MULTI-AGENT FRAMEWORK: Hierarchical foundation model agent architecture that reduces costs by 10-50x through intelligent suppression of expensive foundation model calls. Grounded in R. Arkin's behavior-based robotics and R. Brooks' subsumption architecture, SPL brings 40+ years of proven autonomous systems design
Implementation for the paper "Multiagent Learning Using a Variable Learning Rate"
Multi-Agent Communication in RL systems
MultiAgent Chain of Expert: A Python app using Groq API for dual-model text processing. Gemma analyzes, LLaMA responds, with a modern tkinter GUI. Features history tracking, file I/O, and customizable AI settings. Secure API key handling via .env. MIT License.
Comparative system for multiagent algorithms with different learning strategies. The analysis is carries with the helps of a nash equilibria comparison, the replicator dynamic and a simple grand table with the average reward obtained.
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