def start_client(self):
from ts import JNIClient
from ai.rewardfunction import RewardFunctionInterface, WinLossRewardFunction, ResourceGatherRewardFunction, AttackRewardFunction, ProduceWorkerRewardFunction, ProduceBuildingRewardFunction, ProduceCombatUnitRewardFunction, CloserToEnemyBaseRewardFunction
self.rfs = JArray(RewardFunctionInterface)([
WinLossRewardFunction(),
ResourceGatherRewardFunction(),
ProduceWorkerRewardFunction(),
ProduceBuildingRewardFunction(),
AttackRewardFunction(),
ProduceCombatUnitRewardFunction(),
CloserToEnemyBaseRewardFunction(),])
if self.ai2 is not None:
return JNIClient(self.rfs, os.path.expanduser(self.microrts_path), self.map_path, self.ai2(self.real_utt), self.real_utt)
return JNIClient(self.rfs, os.path.expanduser(self.microrts_path), self.map_path)
def __init__(self, **kwargs):
super().__init__(**kwargs)
from ts import JNIGridnetVecClient
from ai.core import AI
from ai.rewardfunction import RewardFunctionInterface, WinLossRewardFunction, ResourceGatherRewardFunction, AttackRewardFunction, ProduceWorkerRewardFunction, ProduceBuildingRewardFunction, ProduceCombatUnitRewardFunction, CloserToEnemyBaseRewardFunction
self.rfs = JArray(RewardFunctionInterface)([
WinLossRewardFunction(),
ResourceGatherRewardFunction(),
ProduceWorkerRewardFunction(),
ProduceBuildingRewardFunction(),
AttackRewardFunction(),
ProduceCombatUnitRewardFunction(),
# CloserToEnemyBaseRewardFunction(),
])
self.vec_client = JNIGridnetVecClient(
self.num_envs, self.max_steps, self.rfs,
os.path.expanduser(self.microrts_path), self.map_path,
JArray(AI)([ai2(self.real_utt) for ai2 in self.ai2s]),
self.real_utt)
def __init__(self,
num_envs=2,
max_steps=2000,
render_theme=2,
frame_skip=0,
ai2s=[microrts_ai.passiveAI, microrts_ai.passiveAI],
map_path="maps/10x10/basesTwoWorkers10x10.xml",
reward_weight=np.array([0.0, 1.0, 0.0, 0.0, 0.0, 5.0])):
assert num_envs == len(ai2s), "for each environment, a microrts ai should be provided"
self.num_envs = num_envs
self.max_steps = max_steps
self.render_theme = render_theme
self.frame_skip = frame_skip
self.ai2s = ai2s
self.map_path = map_path
self.reward_weight = reward_weight
# read map
self.microrts_path = os.path.join(gym_microrts.__path__[0], 'microrts')
root = ET.parse(os.path.join(self.microrts_path, self.map_path)).getroot()
self.height, self.width = int(root.get("height")), int(root.get("width"))
# launch the JVM
if not jpype._jpype.isStarted():
registerDomain("ts", alias="tests")
registerDomain("ai")
jars = [
"microrts.jar", "Coac.jar", "Droplet.jar", "GRojoA3N.jar",
"Izanagi.jar", "MixedBot.jar", "RojoBot.jar", "TiamatBot.jar", "UMSBot.jar" # "MindSeal.jar"
]
for jar in jars:
jpype.addClassPath(os.path.join(self.microrts_path, jar))
jpype.startJVM(convertStrings=False)
# start microrts client
from rts.units import UnitTypeTable
self.real_utt = UnitTypeTable()
from ai.rewardfunction import RewardFunctionInterface, WinLossRewardFunction, ResourceGatherRewardFunction, AttackRewardFunction, ProduceWorkerRewardFunction, ProduceBuildingRewardFunction, ProduceCombatUnitRewardFunction, CloserToEnemyBaseRewardFunction
self.rfs = JArray(RewardFunctionInterface)([
WinLossRewardFunction(),
ResourceGatherRewardFunction(),
ProduceWorkerRewardFunction(),
ProduceBuildingRewardFunction(),
AttackRewardFunction(),
ProduceCombatUnitRewardFunction(),
# CloserToEnemyBaseRewardFunction(),
])
self.start_client()
# computed properties
# [num_planes_hp(5), num_planes_resources(5), num_planes_player(5),
# num_planes_unit_type(z), num_planes_unit_action(6)]
self.num_planes = [5, 5, 3, len(self.utt['unitTypes'])+1, 6]
self.observation_space = gym.spaces.Box(low=0.0,
high=1.0,
shape=(self.height, self.width,
sum(self.num_planes)),
dtype=np.int32)
self.action_space = gym.spaces.MultiDiscrete([
self.height * self.width,
6, 4, 4, 4, 4,
len(self.utt['unitTypes']),
7 * 7
])