forked from 0ad/0ad

History

wraitii 5473393e30 Add an interface for Reinforcement Learning. Implement a simple HTTP server to start games, receive the gamestate and pass commands to the simulation. This is mainly intended for training reinforcement learning agents in 0 AD. As such, a python client and a small example are included. This option can be enabled using the -rl-interface flag. Patch by: irishninja Reviewed By: wraitii, Itms Fixes #5548 Differential Revision: https://code.wildfiregames.com/D2199 This was SVN commit r23917.		2020-08-01 10:52:59 +00:00
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samples	Add an interface for Reinforcement Learning.	2020-08-01 10:52:59 +00:00
tests	Add an interface for Reinforcement Learning.	2020-08-01 10:52:59 +00:00
zero_ad	Add an interface for Reinforcement Learning.	2020-08-01 10:52:59 +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!