adjsim 2.0.0

An Agent Based Modelling Engine tailored for Reinforcement Learning.

# AdjSim Simulation Framework
[](https://travis-ci.org/SeverTopan/AdjSim) [](https://coveralls.io/github/SeverTopan/AdjSim?branch=master) [](https://www.gnu.org/licenses/gpl-3.0)

A simulation framework. Intended for simulation of ecosystems.


> 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](https://en.wikipedia.org/wiki/Conway%27s_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 demos packadged with AdjSim, ranging from bacteria to moon system simulation.

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### 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.

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### 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](https://en.wikipedia.org/wiki/Q-learning), but more advanced reinforcement learning techniques are coming soon!

## Installing and Running AdjSim

It is reccommended to run AdjSim using virtual python environments provided by Anaconda or Pip. The following describes the installation procedure for each of these.

Clone the GitHub repository.

git clone https://github.com/SeverTopan/AdjSim

Make sure Python 3.5 or greater are installed, then create a new environment with it.

# If using Virtualenv
virtualenv --python=/usr/bin/python3.6 venv
source venv/bin/activate

# If using Anaconda
conda create --name adjsim python=3.6
activate adjsim

Install Dependencies.

python setup.py install

Note: If you run into trouble importing PyQt5 when installing using the setup.py file, try using

pip install -e .