# Learning Complex Multi-Agent Policies in Presence of an Adversary

arXiv: https://arxiv.org/abs/2008.07698 · HTML: https://mlsanity.com/paper/2008.07698/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Aug 2020.

## Built on by (most cited first, 1 of 1)

- [Deception in Social Learning: A Multi-Agent Reinforcement Learning Perspective](https://mlsanity.com/paper/2106.05402/) (2021)

## Abstract (arXiv)

In recent years, there has been some outstanding work on applying deep reinforcement learning to multi-agent settings. Often in such multi-agent scenarios, adversaries can be present. We address the requirements of such a setting by implementing a graph-based multi-agent deep reinforcement learning algorithm. In this work, we consider the scenario of multi-agent deception in which multiple agents need to learn to cooperate and communicate in order to deceive an adversary. We have employed a two-stage learning process to get the cooperating agents to learn such deceptive behaviors. Our experiments show that our approach allows us to employ curriculum learning to increase the number of cooperating agents in the environment and enables a team of agents to learn complex behaviors to successfully deceive an adversary. Keywords: Multi-agent system, Graph neural network, Reinforcement learning