env = MicroRTSStatsRecorder(env, args.gamma)
if args.capture_video:
if idx == 0:
env = Monitor(env, f'videos/{experiment_name}')
env.seed(seed)
env.action_space.seed(seed)
env.observation_space.seed(seed)
return env
return thunk
# envs = VecPyTorch(DummyVecEnv([make_env(args.gym_id, args.seed+i, i) for i in range(args.num_envs)]), device)
envs = MicroRTSVecEnv(
num_envs=args.num_envs,
render_theme=2,
ai2s=[microrts_ai.passiveAI for _ in range(args.num_envs)],
map_path="maps/16x16/basesWorkers16x16.xml",
reward_weight=np.array([10.0, 1.0, 1.0, 0.2, 1.0, 4.0]))
envs = VecMonitor(envs)
envs = VecPyTorch(envs, device)
# if args.prod_mode:
# envs = VecPyTorch(
# SubprocVecEnv([make_env(args.gym_id, args.seed+i, i) for i in range(args.num_envs)], "fork"),
# device
# )
assert isinstance(
envs.action_space,
MultiDiscrete), "only MultiDiscrete action space is supported"
# ALGO LOGIC: initialize agent here:
save_code=True)
writer = SummaryWriter(f"/tmp/{experiment_name}")
# TRY NOT TO MODIFY: seeding
device = torch.device(
'cuda' if torch.cuda.is_available() and args.cuda else 'cpu')
random.seed(args.seed)
np.random.seed(args.seed)
torch.manual_seed(args.seed)
torch.backends.cudnn.deterministic = args.torch_deterministic
envs = MicroRTSVecEnv(
num_envs=args.num_envs,
max_steps=2000,
render_theme=2,
ai2s=[microrts_ai.coacAI for _ in range(args.num_envs-6)] + \
[microrts_ai.randomBiasedAI for _ in range(2)] + \
[microrts_ai.lightRushAI for _ in range(2)] + \
[microrts_ai.workerRushAI for _ in range(2)],
map_path="maps/16x16/basesWorkers16x16.xml",
reward_weight=np.array([10.0, 1.0, 1.0, 0.2, 1.0, 4.0])
)
envs = MicroRTSStatsRecorder(envs, args.gamma)
envs = VecMonitor(envs)
envs = VecPyTorch(envs, device)
if args.capture_video:
envs = VecVideoRecorder(envs,
f'videos/{experiment_name}',
record_video_trigger=lambda x: x % 1000000 == 0,
video_length=2000)
assert isinstance(
envs.action_space,
import numpy as np
import gym
import gym_microrts
import time
from gym_microrts.envs.vec_env import MicroRTSVecEnv
from gym_microrts import microrts_ai
from gym.envs.registration import register
from gym_microrts import Config
try:
env = MicroRTSVecEnv(num_envs=1,
render_theme=2,
ai2s=[microrts_ai.coacAI],
map_path="maps/16x16/basesWorkers16x16.xml",
reward_weight=np.array(
[10.0, 1.0, 1.0, 0.2, 1.0, 4.0]))
# env = gym.make('MicrortsDefeatCoacAIShaped-v3').env
# env = gym.wrappers.RecordEpisodeStatistics(env)
# env.action_space.seed(0)
obs = env.reset()
env.render()
except Exception as e:
e.printStackTrace()
env.action_space.seed(0)
env.reset()
for i in range(10000):
env.render()
action_mask = np.array(env.vec_client.getUnitLocationMasks()).flatten()
time.sleep(0.001)
action = env.action_space.sample()
import torch
import torch.nn as nn
import torch.optim as optim
import numpy as np
from model import Agent, MicroRTSStatsRecorder, VecMonitor, VecPyTorch
import pickle
from gym_microrts.envs.vec_env import MicroRTSVecEnv
from gym_microrts import microrts_ai
import itertools
import os
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
envs = MicroRTSVecEnv(num_envs=1,
render_theme=2,
ai2s=[microrts_ai.workerRushAI for _ in range(1)],
map_path="maps/10x10/basesWorkers10x10.xml",
reward_weight=np.array([10.0, 1.0, 1.0, 0.2, 1.0, 4.0]))
envs = MicroRTSStatsRecorder(envs)
envs = VecMonitor(envs)
envs = VecPyTorch(envs, device)
def enjoy(agent):
rewards = []
next_obs = envs.reset()
while True:
action, logproba, _, _ = agent.get_action(next_obs, envs=envs)
try:
next_obs, rs, ds, infos = envs.step(action.T)
rewards.append(rs)
"lightRushAI": microrts_ai.lightRushAI,
"coacAI": microrts_ai.coacAI,
"naiveMCTSAI": microrts_ai.naiveMCTSAI,
"mixedBot": microrts_ai.mixedBot,
"rojo": microrts_ai.rojo,
"izanagi": microrts_ai.izanagi,
"tiamat": microrts_ai.tiamat,
"droplet": microrts_ai.droplet,
"guidedRojoA3N": microrts_ai.guidedRojoA3N
}
ai_names, ais = list(all_ais.keys()), list(all_ais.values())
ai_match_stats = dict(zip(ai_names, np.zeros((len(ais), 3))))
args.num_envs = len(ais)
envs = MicroRTSVecEnv(num_envs=len(ais),
render_theme=2,
ai2s=ais,
map_path="maps/16x16/basesWorkers16x16A.xml",
reward_weight=np.array([10.0, 1.0, 1.0, 0.2, 1.0, 4.0]))
envs = MicroRTSStatsRecorder(envs)
envs = VecMonitor(envs)
envs = VecPyTorch(envs, device)
# if args.prod_mode:
# envs = VecPyTorch(
# SubprocVecEnv([make_env(args.gym_id, args.seed+i, i) for i in range(args.num_envs)], "fork"),
# device
# )
assert isinstance(
envs.action_space,
MultiDiscrete), "only MultiDiscrete action space is supported"
import numpy as np
import gym
import gym_microrts
from gym_microrts.envs.vec_env import MicroRTSVecEnv
from gym_microrts import microrts_ai
from gym.envs.registration import register
from gym_microrts import Config
try:
env = MicroRTSVecEnv(num_envs=1,
render_theme=2,
ai2=microrts_ai.coacAI,
map_path="maps/16x16/basesWorkers16x16.xml",
reward_weight=np.array(
[10.0, 1.0, 1.0, 0.2, 1.0, 4.0, 0.0]))
# env = gym.make('MicrortsDefeatCoacAIShaped-v3').env
# env = gym.wrappers.RecordEpisodeStatistics(env)
# env.action_space.seed(0)
obs = env.reset()
env.render()
except Exception as e:
e.printStackTrace()
# print("unit_locatiuons are at", np.where(env.get_unit_location_mask()==1)[0])
print("reward is", env.step([[17, 2, 0, 3, 0, 1, 2, 123]])[1])
env.render()
# print("unit_locatiuons are at", np.where(env.get_unit_location_mask()==1)[0])
print("reward is", env.step([[34, 4, 1, 2, 1, 2, 3, 109]])[1])
env.render()