Deep Reinforcement Learning (DRL) algorithms have been successfully applied to a range of challenging simulated continuous control single agent tasks. These methods have further been extended to multiagent domains in cooperative, competitive or mixed environments. This paper primarily focuses on multiagent cooperative settings which can be modeled for several real world problems such as coordination of autonomous vehicles and warehouse robots. However, these systems suffer from several challenges such as, structural credit assignment and partial observability. In this paper, we propose Recurrent Multiagent Deep Deterministic Policy Gradient (RMADDPG) algorithm which extends Multiagent Deep Determinisitic Policy Gradient algorithm - MADDPG \cite{lowe2017multi} by using a recurrent neural network for the actor policy. This helps to address partial observability by maintaining a sequence of past observations which networks learn to preserve in order to solve the POMDP. In addition, we use reward shaping through difference rewards to address structural credit assignment in a partially observed environment. We evaluate the performance of MADDPG and R-MADDPG with and without reward shaping in a Multiagent Particle Environment. We further show that reward shaped RMADDPG outperforms the baseline algorithm MADDPG in a partially observable environmental setting.

Envirionments for NLP multiagent tasks

Tensorflow Implementation of MADDPG in Multiagent-Particle Environments

A ROS workspace to implement complex, intelligent human-robot interaction behaviors in dynamic environments using game-theoretical approaches. Created as a final project for ASE 389: Game Theoretical Modeling of Multiagent Systems, Fall 2022 at the University of Texas - Austin.

Population Based Training of neural networks for multiagent environments

Final Project building multiagent environments from MarlGrid

A multiagent task allocation mechanism for dynamic environments

This repository is meant to house environment extensions to the PettingZoo multiagent reinforcement learning API.

Distributed Task Allocation in Multi-Agent Environments Using Cellular Learning Automata

Multiagent environments that incoporate benevolent deception and implement Open AI Gym interface

this repository is the full python code for the training of a multiagent actor-critic cooperative -competitive environments

- Introduction to intelligent agents. - Agents as intensional systems. Abstract architectures for intelligent agents. - Deductive reasoning agents. - Logics for multiagent systems: modal logic, temporal logic, logics for knowledge e belief. Model checking. - Formal models for BDI (Belief, Desire, Intention) agents: Cohen and Levesque's intetion logic. - Practical Reasoning Agents. Agent Oriented Programming. Concurrent METATEM. GOLOG. - Reactive and hybrid agents. - Multiagent systems. - Multiagent Systems and Societies of Agents. - Communication: Agent Communication Languages (ACL), SInteraction Protocols, Social Semantics and Commitment Protocols. - Multiagent interactions: game theory, strategies and Nash equilibria. - Allocating Scarce Resources: Auctions. - Languages and environments for multiagent systems: Jade, Jason, JaCaMo.

A multiagent system for GONTOR that integrates Unity, WebSockets, and Python. Unity handles the simulation and visualization, Python manages the agents' decision-making and pathfinding logic, and WebSockets enable real-time communication. Ideal for creating collaborative simulations, digital twins, and interactive environments.