Learning Complex Swarm Behaviors by Exploiting Local Communication Protocols with Deep Reinforcement Learning

09/21/2017
by   Maximilian Hüttenrauch, et al.
0

Swarm systems constitute a challenging problem for reinforcement learning (RL) as the algorithm needs to learn decentralized control policies that can cope with limited local sensing and communication abilities of the agents. Although there have been recent advances of deep RL algorithms applied to multi-agent systems, learning communication protocols while simultaneously learning the behavior of the agents is still beyond the reach of deep RL algorithms. However, while it is often difficult to directly define the behavior of the agents, simple communication protocols can be defined more easily using prior knowledge about the given task. In this paper, we propose a number of simple communication protocols that can be exploited by deep reinforcement learning to find decentralized control policies in a multi-robot swarm environment. The protocols are based on histograms that encode the local neighborhood relations of the agents and can also transmit task-specific information, such as the shortest distance and direction to a desired target. In our framework, we use an adaptation of Trust Region Policy Optimization to learn complex collaborative tasks, such as formation building, building a communication link, and pushing an intruder. We evaluate our findings in a simulated 2D-physics environment, and compare the implications of different communication protocols.

READ FULL TEXT
research
07/17/2018

Deep Reinforcement Learning for Swarm Systems

Recently, deep reinforcement learning (RL) methods have been applied suc...
research
06/06/2022

Learning Generalized Wireless MAC Communication Protocols via Abstraction

To tackle the heterogeneous requirements of beyond 5G (B5G) and future 6...
research
10/19/2018

Social Influence as Intrinsic Motivation for Multi-Agent Deep Reinforcement Learning

We propose a unified mechanism for achieving coordination and communicat...
research
09/16/2021

Decentralized Control of Quadrotor Swarms with End-to-end Deep Reinforcement Learning

We demonstrate the possibility of learning drone swarm controllers that ...
research
06/17/2018

Learning Policy Representations in Multiagent Systems

Modeling agent behavior is central to understanding the emergence of com...
research
08/31/2021

BotNet: A Simulator for Studying the Effects of Accurate Communication Models on Multi-agent and Swarm Control

Decentralized control in multi-robot systems is dependent on accurate an...
research
06/15/2020

ForMIC: Foraging via Multiagent RL with Implicit Communication

Multi-agent foraging (MAF) involves distributing a team of agents to sea...

Please sign up or login with your details

Forgot password? Click here to reset