A selection of state-of-the-art research materials on trajectory prediction

Multi-Agent Reinforcement Learning (MARL) papers

A Collection of Multi-Agent Reinforcement Learning (MARL) Resources

[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 with various normalization techniques.

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.

Implementation for the paper "Multiagent Learning Using a Variable Learning Rate"

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

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.

A Study on Reinforcement Learning in Starcraft Game Platform as a Collaborative Researcher of Samsung Company.

Scenario builder for the multi-agent system of intelligent mobile robots

Advanced Python app for text processing using Groq API. Features dual-model AI with Gemma and LLaMA, plus a sleek GUI. 🚀🌐

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.

Resource Abstraction (AAMAS 2016)

Explore a unique Unity-powered simulation where food appears randomly and a set of 5 characters seeks it optimally. Melding advanced simulations with a Python backend, NomNomSimulator offers a glimpse into intelligent navigation. Dive into the future of dynamic food-finding adventures.

Multi-agent learning project