0ad/source/tools/rlclient/python/tests/test_actions.py
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

100 lines
3.3 KiB
Python

import zero_ad
import json
import math
from os import path
game = zero_ad.ZeroAD('http://localhost:6000')
scriptdir = path.dirname(path.realpath(__file__))
with open(path.join(scriptdir, '..', 'samples', 'arcadia.json'), 'r') as f:
config = f.read()
def dist (p1, p2):
return math.sqrt(sum((math.pow(x2 - x1, 2) for (x1, x2) in zip(p1, p2))))
def center(units):
sum_position = map(sum, zip(*map(lambda u: u.position(), units)))
return [x/len(units) for x in sum_position]
def closest(units, position):
dists = (dist(unit.position(), position) for unit in units)
index = 0
min_dist = next(dists)
for (i, d) in enumerate(dists):
if d < min_dist:
index = i
min_dist = d
return units[index]
def test_construct():
state = game.reset(config)
female_citizens = state.units(owner=1, type='female_citizen')
house_tpl = 'structures/spart_house'
house_count = len(state.units(owner=1, type=house_tpl))
x = 680
z = 640
build_house = zero_ad.actions.construct(female_citizens, house_tpl, x, z, autocontinue=True)
# Check that they start building the house
state = game.step([build_house])
while len(state.units(owner=1, type=house_tpl)) == house_count:
state = game.step()
def test_gather():
state = game.reset(config)
female_citizen = state.units(owner=1, type='female_citizen')[0]
trees = state.units(owner=0, type='tree')
nearby_tree = closest(state.units(owner=0, type='tree'), female_citizen.position())
collect_wood = zero_ad.actions.gather([female_citizen], nearby_tree)
state = game.step([collect_wood])
while len(state.unit(female_citizen.id()).data['resourceCarrying']) == 0:
state = game.step()
def test_train():
state = game.reset(config)
civic_centers = state.units(owner=1, type="civil_centre")
spearman_type = 'units/spart_infantry_spearman_b'
spearman_count = len(state.units(owner=1, type=spearman_type))
train_spearmen = zero_ad.actions.train(civic_centers, spearman_type)
state = game.step([train_spearmen])
while len(state.units(owner=1, type=spearman_type)) == spearman_count:
state = game.step()
def test_walk():
state = game.reset(config)
female_citizens = state.units(owner=1, type='female_citizen')
x = 680
z = 640
initial_distance = dist(center(female_citizens), [x, z])
walk = zero_ad.actions.walk(female_citizens, x, z)
state = game.step([walk])
distance = initial_distance
while distance >= initial_distance:
state = game.step()
female_citizens = state.units(owner=1, type='female_citizen')
distance = dist(center(female_citizens), [x, z])
def test_attack():
state = game.reset(config)
units = state.units(owner=1, type='cavalry')
target = state.units(owner=2, type='female_citizen')[0]
initial_health = target.health()
state = game.step([zero_ad.actions.reveal_map()])
attack = zero_ad.actions.attack(units, target)
state = game.step([attack])
while state.unit(target.id()).health() >= initial_health:
state = game.step()
def test_debug_print():
state = game.reset(config)
debug_print = zero_ad.actions.debug_print('hello world!!')
state = game.step([debug_print])
def test_chat():
state = game.reset(config)
chat = zero_ad.actions.chat('hello world!!')
state = game.step([chat])