def main():
env_name = 'doom_battle'
env = create_env(env_name, cfg=default_cfg(env=env_name))
env.reset()
done = False
while not done:
env.render()
obs, rew, done, info = env.step(env.action_space.sample())
log.info('Done!')
def test_minigrid_env(self):
env_name = 'MiniGrid-Empty-Random-5x5-v0'
env = create_env(env_name, cfg=default_cfg(env=env_name))
log.info('Env action space: %r', env.action_space)
log.info('Env obs space: %r', env.observation_space)
env.reset()
total_rew = 0
for i in range(1000):
obs, rew, done, info = env.step(env.action_space.sample())
total_rew += rew
if done:
env.reset()
def main():
env_name = 'doom_health_gathering'
cfg = default_cfg(env=env_name)
env = make_doom_env_impl(doom_env_by_name(env_name),
cfg=cfg,
custom_resolution='1280x720')
# cfg.num_agents = 1
# cfg.num_bots = 7
# # cfg.num_humans = 1
# env = make_doom_multiplayer_env(doom_env_by_name(env_name), cfg=cfg, custom_resolution='1280x720')
return VizdoomEnv.play_human_mode(env, skip_frames=2, num_actions=15)
def main():
parser = argparse.ArgumentParser(
formatter_class=argparse.ArgumentDefaultsHelpFormatter)
parser.add_argument('--env', type=str, default=None, required=True)
parser.add_argument('--demo_path', type=str, default=None, required=True)
args = parser.parse_args()
spec = doom_env_by_name(args.env)
cfg = default_cfg(env=args.env)
if spec.num_agents <= 1:
env = make_doom_env(args.env, cfg=cfg, custom_resolution='1280x720')
else:
env = make_doom_env_impl(
spec,
cfg=cfg,
custom_resolution='1280x720',
player_id=0,
num_agents=spec.num_agents,
max_num_players=spec.num_agents,
num_bots=spec.num_bots,
)
mode = 'replay'
env.unwrapped.mode = mode
env.unwrapped.initialize()
game = env.unwrapped.game
game.replay_episode(args.demo_path)
frames_dir = args.demo_path + '_frames'
if os.path.exists(frames_dir):
shutil.rmtree(frames_dir)
os.makedirs(frames_dir)
frame_id = 0
while not game.is_episode_finished():
# Use advance_action instead of make_action.
game.advance_action()
img = env.render(mode='rgb_array')
frame_name = f'{frame_id:05d}.png'
img = cv2.cvtColor(img, cv2.COLOR_RGB2BGR)
if img is not None:
cv2.imwrite(join(frames_dir, frame_name), img)
frame_id += 1
r = game.get_last_reward()
log.debug('Reward %.3f at frame %d', r, frame_id)
game.close()
def test_voxel_env(self):
env_name = 'voxel_env_Sokoban'
env = create_env(env_name, cfg=default_cfg(env=env_name))
log.info('Env action space: %r', env.action_space)
log.info('Env obs space: %r', env.observation_space)
env.reset()
total_rew = 0
for i in range(1000):
obs, rew, done, info = env.step(
[env.action_space.sample() for _ in range(env.num_agents)])
total_rew += sum(rew)
log.info('Total rew: %.3f', total_rew)
def make_env_func(env_config):
print('Creating env!!!')
cfg = default_cfg(env=spec.name)
cfg.pixel_format = 'HWC' # tensorflow models expect HWC by default
if 'res_w' in env_config:
cfg.res_w = env_config['res_w']
if 'res_h' in env_config:
cfg.res_h = env_config['res_h']
if 'renderer' in env_config:
cfg.renderer = env_config['renderer']
if 'dmlab_throughput_benchmark' in env_config:
cfg.renderer = env_config['dmlab_throughput_benchmark']
env = make_dmlab_env(spec.name,
env_config=env_config,
cfg=cfg,
**kwargs)
return env
def forward_pass(device_type):
env_name = 'atari_breakout'
cfg = default_cfg(algo='APPO', env=env_name)
cfg.actor_critic_share_weights = True
cfg.hidden_size = 128
cfg.use_rnn = True
cfg.env_framestack = 4
env = create_env(env_name, cfg=cfg)
torch.set_num_threads(1)
torch.backends.cudnn.benchmark = True
actor_critic = create_actor_critic(cfg, env.observation_space,
env.action_space)
device = torch.device(device_type)
actor_critic.to(device)
timing = Timing()
with timing.timeit('all'):
batch = 128
with timing.add_time('input'):
# better avoid hardcoding here...
observations = dict(
obs=torch.rand([batch, 4, 84, 84]).to(device))
rnn_states = torch.rand([batch,
get_hidden_size(cfg)]).to(device)
n = 200
for i in range(n):
with timing.add_time('forward'):
output = actor_critic(observations, rnn_states)
log.debug('Progress %d/%d', i, n)
log.debug('Timing: %s', timing)
def make_env_func(env_config):
print('Creating env!!!')
cfg = default_cfg(env=spec.name)
cfg.pixel_format = 'HWC' # tensorflow models expect HWC by default
if 'skip_frames' in env_config:
cfg.env_frameskip = env_config['skip_frames']
if 'res_w' in env_config:
cfg.res_w = env_config['res_w']
if 'res_h' in env_config:
cfg.res_h = env_config['res_h']
if 'wide_aspect_ratio' in env_config:
cfg.wide_aspect_ratio = env_config['wide_aspect_ratio']
env = make_doom_env(spec.name,
env_config=env_config,
cfg=cfg,
**kwargs)
# we lock the global mutex here, otherwise Doom instances may crash on first reset when too many of them are reset simultaneously
lock = FileLock(DOOM_LOCK_PATH)
attempt = 0
while True:
attempt += 1
try:
with lock.acquire(timeout=10):
print('Env created, resetting...')
env.reset()
print('Env reset completed! Config:', env_config)
break
except Timeout:
print(
'Another instance of this application currently holds the lock, attempt:',
attempt)
return env
def default_doom_cfg():
return default_cfg(env='doom_env')
def make_env(env_config):
from sample_factory.envs.dmlab.dmlab_env import make_dmlab_env
return make_dmlab_env('dmlab_nonmatch',
cfg=default_cfg(env='dmlab_nonmatch'),
env_config=None)
def make_env(env_config):
from sample_factory.envs.atari.atari_utils import make_atari_env
return make_atari_env('atari_breakout',
cfg=default_cfg(env='atari_breakout'))