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

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

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.

Multi-Agent Communication in RL systems

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.

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

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

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.

Resource Abstraction (AAMAS 2016)

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.

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.

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

Multi-agent learning project