forked from 0ad/0ad

History

wraitii df18102e96 Add an 'Evaluate' RL endpoint, intended for dynamic game balancing This adds a new endpoint to the RL interface for evaluating custom JavaScript. When combined with the ability to create arbitrary modifiers, this provides the required functionality for exploring quantitative game balancing. Patch By: irishninja Fixes #5981 Differential Revision: https://code.wildfiregames.com/D3479 This was SVN commit r24962.		2021-02-28 12:16:32 +00:00
..
samples	RL: Update template paths & remove debug-print.	2021-02-01 14:53:40 +00:00
tests	Add an 'Evaluate' RL endpoint, intended for dynamic game balancing	2021-02-28 12:16:32 +00:00
zero_ad	Add an 'Evaluate' RL endpoint, intended for dynamic game balancing	2021-02-28 12:16:32 +00:00
README.md	Add an interface for Reinforcement Learning.	2020-08-01 10:52:59 +00:00
requirements-dev.txt	Add an interface for Reinforcement Learning.	2020-08-01 10:52:59 +00:00
setup.py	Add an interface for Reinforcement Learning.	2020-08-01 10:52:59 +00:00

README.md

0 AD Python Client

This directory contains zero_ad, a python client for 0 AD which enables users to control the environment headlessly.

Installation

zero_ad can be installed with pip by running the following from the current directory:

pip install .

Development dependencies can be installed with pip install -r requirements-dev.txt. Tests are using pytest and can be run with python -m pytest.

Basic Usage

If there is not a running instance of 0 AD, first start 0 AD with the RL interface enabled:

pyrogenesis --rl-interface=127.0.0.1:6000

Next, the python client can be connected with:

import zero_ad
from zero_ad import ZeroAD

game = ZeroAD('http://localhost:6000')

A map can be loaded with:

with open('./samples/arcadia.json', 'r') as f:
    arcadia_config = f.read()

state = game.reset(arcadia_config)

where ./samples/arcadia.json is the path to a game configuration JSON (included in the first line of the commands.txt file in a game replay directory) and state contains the initial game state for the given map. The game engine can be stepped (optionally applying actions at each step) with:

state = game.step()

For example, enemy units could be attacked with:

my_units = state.units(owner=1)
enemy_units = state.units(owner=2)
actions = [zero_ad.actions.attack(my_units, enemy_units[0])]
state = game.step(actions)

For a more thorough example, check out samples/simple-example.py!