Skip to main content

An Agent Based Modelling Engine tailored for Reinforcement Learning.

Project description

Build Status Coverage Status License: GPL v3

Designed and developed by Sever Topan

Features

Engine

At its core, AdjSim is an agent-based modelling engine. It allows users to define simulation environments through which agents interact through ability casting and timestep iteration. The framework is targeted towards agents that behave intelligently, for example a bacterium chasing down food. However, the framework is extremely flexible - from enabling physics simulation to defining an environment in which Conway’s Game of Life plays out! AdjSim aims to be a foundational architecture on top of which reinforcement learning can be built.

Graphical Simulation Representation

The simulation can be viewed in real time as it unfolds, with graphics are rendered and animated using PyQt5. Below are four of the distinct examples packadged with AdjSim, ranging from bacteria to moon system simulation.

Bacteria Demo Predator Prey Demo
GOL Demo Jupiter Demo

Post Simulation Analysis Tools

Agent properties can be marked for tracking during simulation, allowing for viewing the results of these values once the simulation completes. For example, we can track the population of each different type of agent, or the efficacy of the agent’s ability to meet its intelligence module-defined goals.

| QLearning Graph| Predator Prey Graph | |:————-:|:————-:|

Intelligence Module

Perhaps the most computationally interesting aspect of AdjSim lies in its intelligence module. It allows agents to set goals (for example, the goal of a bacterium may be to maximize its calories), and assess its actions in terms of its ability to meet its goals. This allows the agents to learn which actions are best used in a given situation. Currently the intelligence module implements Q-Learning, but more advanced reinforcement learning techniques are coming soon!

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

adjsim-2.1.0.tar.gz (24.6 kB view hashes)

Uploaded source

Built Distribution

adjsim-2.1.0-py3-none-any.whl (25.9 kB view hashes)

Uploaded py3

Supported by

AWS AWS Cloud computing Datadog Datadog Monitoring Facebook / Instagram Facebook / Instagram PSF Sponsor Fastly Fastly CDN Google Google Object Storage and Download Analytics Huawei Huawei PSF Sponsor Microsoft Microsoft PSF Sponsor NVIDIA NVIDIA PSF Sponsor Pingdom Pingdom Monitoring Salesforce Salesforce PSF Sponsor Sentry Sentry Error logging StatusPage StatusPage Status page