Sim-Env: Decoupling OpenAI Gym Environments from Simulation Models

02/19/2021
by   Andreas Schuderer, et al.
0

Reinforcement learning (RL) is one of the most active fields of AI research. Despite the interest demonstrated by the research community in reinforcement learning, the development methodology still lags behind, with a severe lack of standard APIs to foster the development of RL applications. OpenAI Gym is probably the most used environment to develop RL applications and simulations, but most of the abstractions proposed in such a framework are still assuming a semi-structured methodology. This is particularly relevant for agent-based models whose purpose is to analyse adaptive behaviour displayed by self-learning agents in the simulation. In order to bridge this gap, we present a workflow and tools for the decoupled development and maintenance of multi-purpose agent-based models and derived single-purpose reinforcement learning environments, enabling the researcher to swap out environments with ones representing different perspectives or different reward models, all while keeping the underlying domain model intact and separate. The Sim-Env Python library generates OpenAI-Gym-compatible reinforcement learning environments that use existing or purposely created domain models as their simulation back-ends. Its design emphasizes ease-of-use, modularity and code separation.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
03/26/2022

Dynamic Noises of Multi-Agent Environments Can Improve Generalization: Agent-based Models meets Reinforcement Learning

We study the benefits of reinforcement learning (RL) environments based ...
research
04/04/2022

Reinforcement Learning Agents in Colonel Blotto

Models and games are simplified representations of the world. There are ...
research
09/18/2019

Segregation Dynamics with Reinforcement Learning and Agent Based Modeling

Societies are complex. Properties of social systems can be explained by ...
research
10/12/2022

Phantom – A RL-driven multi-agent framework to model complex systems

Agent based modelling (ABM) is a computational approach to modelling com...
research
02/09/2023

RayNet: A Simulation Platform for Developing Reinforcement Learning-Driven Network Protocols

Reinforcement Learning has gained significant momentum in the developmen...
research
06/08/2022

Sim2real for Reinforcement Learning Driven Next Generation Networks

The next generation of networks will actively embrace artificial intelli...
research
01/24/2021

A Methodology for the Development of RL-Based Adaptive Traffic Signal Controllers

This article proposes a methodology for the development of adaptive traf...

Please sign up or login with your details

Forgot password? Click here to reset