def batch_evaluate(agent, env_name, seed, episodes, return_obss_actions=False, pixel=False):
num_envs = min(256, episodes)
envs = []
for i in range(num_envs):
env = gym.make(env_name)
if pixel:
env = RGBImgPartialObsWrapper(env)
envs.append(env)
env = ManyEnvs(envs)
logs = {
"num_frames_per_episode": [],
"return_per_episode": [],
"observations_per_episode": [],
"actions_per_episode": [],
"seed_per_episode": []
}
for i in tqdm(range((episodes + num_envs - 1) // num_envs)):
seeds = range(seed + i * num_envs, seed + (i + 1) * num_envs)
env.seed(seeds)
# Reset agent.
if hasattr(agent, 'reset'):
agent.reset()
many_obs = env.reset()
cur_num_frames = 0
num_frames = np.zeros((num_envs,), dtype='int64')
returns = np.zeros((num_envs,))
already_done = np.zeros((num_envs,), dtype='bool')
if return_obss_actions:
obss = [[] for _ in range(num_envs)]
actions = [[] for _ in range(num_envs)]
while (num_frames == 0).any():
action = agent.act_batch(many_obs)['action']
if return_obss_actions:
for i in range(num_envs):
if not already_done[i]:
obss[i].append(many_obs[i])
actions[i].append(action[i].item())
many_obs, reward, done, _ = env.step(action)
agent.analyze_feedback(reward, done)
done = np.array(done)
just_done = done & (~already_done)
returns += reward * just_done
cur_num_frames += 1
num_frames[just_done] = cur_num_frames
already_done[done] = True
logs["num_frames_per_episode"].extend(list(num_frames))
logs["return_per_episode"].extend(list(returns))
logs["seed_per_episode"].extend(list(seeds))
if return_obss_actions:
logs["observations_per_episode"].extend(obss)
logs["actions_per_episode"].extend(actions)
return logs
def __init__(self, name, horizon=None, gamma=0.99, history_length=4,
fixed_seed=None, use_pixels=False):
"""
Constructor.
Args:
name (str): name of the environment;
horizon (int, None): the horizon;
gamma (float, 0.99): the discount factor;
history_length (int, 4): number of frames to form a state;
fixed_seed (int, None): if passed, it fixes the seed of the
environment at every reset. This way, the environment is fixed
rather than procedurally generated;
use_pixels (bool, False): if True, MiniGrid's default 7x7x3
observations is converted to an image of resolution 56x56x3.
"""
# MDP creation
self._not_pybullet = True
self._first = True
env = gym.make(name)
obs_high = 10.
if use_pixels:
env = RGBImgPartialObsWrapper(env) # Get pixel observations
obs_high = 255.
env = ImgObsWrapper(env) # Get rid of the 'mission' field
self.env = env
self._fixed_seed = fixed_seed
self._img_size = env.observation_space.shape[0:2]
self._history_length = history_length
# Get the default horizon
if horizon is None:
horizon = self.env.max_steps
# MDP properties
action_space = Discrete(self.env.action_space.n)
observation_space = Box(
low=0., high=obs_high, shape=(history_length, self._img_size[1], self._img_size[0]))
self.env.max_steps = horizon + 1 # Hack to ignore gym time limit (do not use np.inf, since MiniGrid returns r(t) = 1 - 0.9t/T)
mdp_info = MDPInfo(observation_space, action_space, gamma, horizon)
Environment.__init__(self, mdp_info)
self._state = None
parser.add_argument("--save-interval", type=int, default=50,
help="number of updates between two saves (default: 50, 0 means no saving)")
args = parser.parse_args()
utils.seed(args.seed)
if os.environ.get("TORCH_DETECT_ANOMALY", None):
torch.set_anomaly_enabled(True)
# Generate environments
envs = []
use_pixel = 'pixel' in args.arch
for i in range(args.procs):
env = gym.make(args.env)
if use_pixel:
env = RGBImgPartialObsWrapper(env)
env.seed(100 * args.seed + i)
envs.append(env)
# Define model name
suffix = datetime.datetime.now().strftime("%y-%m-%d-%H-%M-%S")
instr = args.instr_arch if args.instr_arch else "noinstr"
mem = "mem" if not args.no_mem else "nomem"
model_name_parts = {
'env': args.env,
'algo': args.algo,
'arch': args.arch,
'instr': instr,
'mem': mem,
'seed': args.seed,
'info': '',
"pagedown": "drop",
" ": "toggle"
}
assert args.model is not None or args.demos is not None, "--model or --demos must be specified."
# if args.seed is None:
# args.seed = 0 if args.model is not None else 1
# Set seed for all randomness sources
utils.seed(args.seed)
# Generate environment
env = gym.make(args.env)
if args.model is not None and 'pixel' in args.model:
env = RGBImgPartialObsWrapper(env)
env.seed(args.seed)
global obs
obs = env.reset()
print("Mission: {}".format(obs["mission"]))
# Define agent
agent = utils.load_agent(env, args.model, args.demos, args.demos_origin,
args.argmax, args.env)
# Run the agent
done = True
action = None
def make_env(args, dream_env: bool = False, seed: Optional[int] = None,
keep_image: bool = False, wrap_rnn: bool = True, load_model: bool = True):
# Prepares an environment that matches the expected format:
# - The environment returns a 64x64 image in observation["image"]
# and camera data (x, y, z, pitch, yaw) in observation["camera"]
# - If wrapped in the RNN, observation["features"] returns the RNN output to be used for the controller
# - A dream environment simulates the actual environment using the RNN. It never returns an image
# (because the actual environment doesn't get run) and only returns the features
# - A wrapped environment always returns the features, and can return the original image when keep_image is True
full_episode = args.full_episode
# Initialize VAE and MDNRNN networks
if dream_env or wrap_rnn:
features_mode = FeatureMode.MODE_ZCH if args.state_space == 2 else FeatureMode.MODE_ZH
if args.use_gqn:
encoder = GenerativeQueryNetwork(args.gqn_x_dim, args.gqn_r_dim,
args.gqn_h_dim, args.gqn_z_dim, args.gqn_l, name="gqn")
encoder_path = get_path(args, "tf_gqn")
else:
encoder = CVAE(args)
encoder_path = get_path(args, "tf_vae")
rnn = MDNRNN(args)
rnn_path = get_path(args, "tf_rnn")
# TODO: Is this still needed? Do we ever NOT load the model?
if load_model:
encoder.load_weights(str(encoder_path))
rnn.load_weights(str(rnn_path))
if dream_env:
assert keep_image is False, "Dream environment doesn't support image observations"
import json
initial_z_dir = get_path(args, "tf_initial_z")
if args.use_gqn:
initial_z_path = initial_z_dir / "initial_z_gqn.json"
with open(str(initial_z_path), 'r') as f:
initial_z = json.load(f)
else:
initial_z_path = initial_z_dir / "initial_z_vae.json"
with open(str(initial_z_path), 'r') as f:
[initial_mu, initial_logvar] = json.load(f)
# This could probably be done more efficiently
initial_z = np.array([list(elem) for elem in zip(initial_mu, initial_logvar)], dtype=np.float)
# Create dream environment
# noinspection PyUnboundLocalVariable
env = DreamEnv(initial_z, args.z_size, rnn, features_mode)
else:
# Create real environment
kwargs = {}
if args.env_name.startswith("VizdoomTakeCover"):
kwargs["position"] = True # Include position data as observation for Vizdoom environment
print("Making environment {}...".format(args.env_name))
env = gym.make(args.env_name, **kwargs)
print("Raw environment:", env)
from gym.envs.box2d import CarRacing
from vizdoomgym.envs import VizdoomTakeCover
from gym_minigrid.minigrid import MiniGridEnv
if isinstance(env.unwrapped, CarRacing):
# Accept actions in the required format
env = CarRacingActionWrapper(env)
# Transform CarRacing observations into expected format and add camera data
env = CarRacingObservationWrapper(env)
# Cut off "status bar" at the bottom of CarRacing observation (copied from original paper)
env = ClipPixelObservationWrapper(env, (slice(84),))
elif isinstance(env.unwrapped, VizdoomTakeCover):
# Accept actions in the required format
env = VizdoomTakeCoverActionWrapper(env)
# Transform Vizdoom observations into expected format
env = VizdoomObservationWrapper(env)
# Cut off "status bar" at the bottom of the screen (copied from original paper)
env = ClipPixelObservationWrapper(env, (slice(400),))
elif isinstance(env.unwrapped, MiniGridEnv):
from gym_minigrid.wrappers import RGBImgPartialObsWrapper
# Accept actions in the required format
env = MiniGridActionWrapper(env)
# Get RGB image observations from the agent's viewpoint
# (7x7 grid of tiles, with tile size 9 this results in a 63x63 image)
env = RGBImgPartialObsWrapper(env, tile_size=9)
# Add camera data to the observation
env = MiniGridObservationWrapper(env)
# Pad image to 64x64 to match the requirements (in effect just adding one row at the right and bottom edge
# with repeated values from the edge)
env = PadPixelObservationWrapper(env, target_size=64)
else:
env = PixelObservationWrapper(env, pixel_keys=("image",))
if env.observation_space["image"].shape[:2] != (64, 64):
# Resize image to 64x64
env = ResizePixelObservationWrapper(env, size=(64, 64))
# Wrap in RNN to add features to observation
if wrap_rnn:
# noinspection PyUnboundLocalVariable
env = MDNRNNWrapper(env, encoder, rnn, keep_image=keep_image, features_mode=features_mode)
# TODO: Is this needed? It was only ever implemented for CarRacing and didn't work
# Force done=False if full_episode is True
if full_episode:
env = NoEarlyStopWrapper(env)
# Set seed if given
if seed is not None:
env.seed(seed)
print("Wrapped environment:", env)
return env
if reward_mean > 500:
break
def play(self, num_episodes, render=True):
"""Test the trained agent.
"""
for episode in range(num_episodes):
state = self.env.reset()
total_reward = 0.0
while True:
if render:
self.env.render()
action = self.get_action(state)
state, reward, done, _ = self.env.step(action)
total_reward += reward
if done:
print(
f"Total reward: {total_reward} in episode {episode + 1}"
)
break
if __name__ == "__main__":
env = gym.make("MiniGrid-Empty-8x8-v0")
env = RGBImgPartialObsWrapper(env) # Get pixel observations
env = ImgObsWrapper(env) # Get rid of the 'mission' field
agent = Agent(env)
print("Number of actions: ", agent.actions)
agent.train(percentile=99.9, num_iterations=64, num_episodes=128)
agent.play(num_episodes=3)
)
parser.add_argument(
"--tile_size",
type=int,
help="size at which to render tiles",
default=32
)
parser.add_argument(
'--agent_view',
default=False,
help="draw the agent sees (partially observable view)",
action='store_true'
)
args = parser.parse_args()
env = gym.make(args.env)
if args.agent_view:
env = RGBImgPartialObsWrapper(env)
env = ImgObsWrapper(env)
print(dijkstras(env))
window = Window('gym_minigrid - ' + args.env)
window.reg_key_handler(key_handler)
reset()
# Blocking event loop
window.show(block=True)
def generate_demos(n_episodes, valid, seed, shift=0):
utils.seed(seed)
# Generate environment
env = gym.make(args.env)
use_pixels = args.pixels
if use_pixels:
env = RGBImgPartialObsWrapper(env)
agent = utils.load_agent(env, args.model, args.demos, 'agent', args.argmax,
args.env)
demos_path = utils.get_demos_path(args.demos, args.env, 'agent', valid)
demos = []
checkpoint_time = time.time()
just_crashed = False
while True:
if len(demos) == n_episodes:
break
done = False
if just_crashed:
logger.info(
"reset the environment to find a mission that the bot can solve"
)
env.reset()
else:
env.seed(seed + len(demos))
obs = env.reset()
agent.on_reset()
actions = []
mission = obs["mission"]
images = []
directions = []
try:
while not done:
action = agent.act(obs)['action']
if isinstance(action, torch.Tensor):
action = action.item()
new_obs, reward, done, _ = env.step(action)
agent.analyze_feedback(reward, done)
actions.append(action)
images.append(obs['image'])
if use_pixels:
directions.append(None)
else:
directions.append(obs['direction'])
obs = new_obs
if reward > 0 and (args.filter_steps == 0
or len(images) <= args.filter_steps):
demos.append((mission, blosc.pack_array(np.array(images)),
directions, actions))
just_crashed = False
if reward == 0:
if args.on_exception == 'crash':
raise Exception(
"mission failed, the seed is {}".format(seed +
len(demos)))
just_crashed = True
logger.info("mission failed")
except (Exception, AssertionError):
if args.on_exception == 'crash':
raise
just_crashed = True
logger.exception("error while generating demo #{}".format(
len(demos)))
continue
if len(demos) and len(demos) % args.log_interval == 0:
now = time.time()
demos_per_second = args.log_interval / (now - checkpoint_time)
to_go = (n_episodes - len(demos)) / demos_per_second
logger.info(
"demo #{}, {:.3f} demos per second, {:.3f} seconds to go".
format(len(demos) - 1, demos_per_second, to_go))
checkpoint_time = now
# Save demonstrations
if args.save_interval > 0 and len(
demos) < n_episodes and len(demos) % args.save_interval == 0:
logger.info("Saving demos...")
utils.save_demos(demos, demos_path)
logger.info("{} demos saved".format(len(demos)))
# print statistics for the last 100 demonstrations
print_demo_lengths(demos[-100:])
# Save demonstrations
logger.info("Saving demos...")
utils.save_demos(demos, demos_path)
logger.info("{} demos saved".format(len(demos)))
print_demo_lengths(demos[-100:])
def batch_evaluate(agent,
env_name,
seed,
episodes,
return_obss_actions=False,
pixel=False,
monitor_gym=False,
pairs_dict=None,
model_path=None):
num_envs = min(256, episodes)
envs = []
for i in range(num_envs):
if '_c' in env_name:
env = gym.make(env_name, pairs_dict=pairs_dict, test_mode=True)
else:
env = gym.make(env_name)
if pixel:
env = RGBImgPartialObsWrapper(env)
if monitor_gym:
demo_path = os.path.join(model_path, 'gym_demos')
if not i % 64:
env = Monitor(
env,
demo_path,
video_callable=lambda episode_id: episode_id == 1,
force=True)
else:
env = Monitor(env, demo_path, video_callable=False, force=True)
envs.append(env)
env = ManyEnvs(envs)
logs = {
"num_frames_per_episode": [],
"return_per_episode": [],
"observations_per_episode": [],
"actions_per_episode": [],
"seed_per_episode": [],
"seen_missions": [env.mission for env in envs]
}
for i in range((episodes + num_envs - 1) // num_envs):
seeds = range(seed + i * num_envs, seed + (i + 1) * num_envs)
env.seed(seeds)
many_obs = env.reset()
cur_num_frames = 0
num_frames = np.zeros((num_envs, ), dtype='int64')
returns = np.zeros((num_envs, ))
already_done = np.zeros((num_envs, ), dtype='bool')
if return_obss_actions:
obss = [[] for _ in range(num_envs)]
actions = [[] for _ in range(num_envs)]
while (num_frames == 0).any():
action = agent.act_batch(many_obs)['action']
if return_obss_actions:
for i in range(num_envs):
if not already_done[i]:
obss[i].append(many_obs[i])
actions[i].append(action[i].item())
many_obs, reward, done, _ = env.step(action)
agent.analyze_feedback(reward, done)
done = np.array(done)
just_done = done & (~already_done)
returns += reward * just_done
cur_num_frames += 1
num_frames[just_done] = cur_num_frames
already_done[done] = True
logs["num_frames_per_episode"].extend(list(num_frames))
logs["return_per_episode"].extend(list(returns))
logs["seed_per_episode"].extend(list(seeds))
if return_obss_actions:
logs["observations_per_episode"].extend(obss)
logs["actions_per_episode"].extend(actions)
return logs
def BobEnv(size):
return ImgObsWrapper(RGBImgPartialObsWrapper(_BobEnv(size)))
def __init__(
self,
args,
):
self.args = args
utils.seed(self.args.seed)
self.val_seed = self.args.val_seed
self.use_pixel = 'pixels' in args.model
# args.env is a list when training on multiple environments
if getattr(args, 'multi_env', None):
self.env = [gym.make(item) for item in args.multi_env]
if self.use_pixel:
self.env = [RGBImgPartialObsWrapper(e) for e in self.env]
self.train_demos = []
for demos, episodes in zip(args.multi_demos, args.multi_episodes):
demos_path = utils.get_demos_path(demos,
None,
None,
valid=False)
logger.info('loading {} of {} demos'.format(episodes, demos))
train_demos = utils.load_demos(demos_path)
logger.info('loaded demos')
if episodes > len(train_demos):
raise ValueError(
"there are only {} train demos in {}".format(
len(train_demos), demos))
self.train_demos.extend(train_demos[:episodes])
logger.info('So far, {} demos loaded'.format(
len(self.train_demos)))
self.val_demos = []
for demos, episodes in zip(args.multi_demos, [args.val_episodes] *
len(args.multi_demos)):
demos_path_valid = utils.get_demos_path(demos,
None,
None,
valid=True)
logger.info('loading {} of {} valid demos'.format(
episodes, demos))
valid_demos = utils.load_demos(demos_path_valid)
logger.info('loaded demos')
if episodes > len(valid_demos):
logger.info(
'Using all the available {} demos to evaluate valid. accuracy'
.format(len(valid_demos)))
self.val_demos.extend(valid_demos[:episodes])
logger.info('So far, {} valid demos loaded'.format(
len(self.val_demos)))
logger.info('Loaded all demos')
observation_space = self.env[0].observation_space
action_space = self.env[0].action_space
else:
self.env = gym.make(self.args.env)
if self.use_pixel:
self.env = RGBImgPartialObsWrapper(self.env)
demos_path = utils.get_demos_path(args.demos,
args.env,
args.demos_origin,
valid=False)
demos_path_valid = utils.get_demos_path(args.demos,
args.env,
args.demos_origin,
valid=True)
logger.info('loading demos')
self.train_demos = utils.load_demos(demos_path)
logger.info('loaded demos')
if args.episodes:
if args.episodes > len(self.train_demos):
raise ValueError("there are only {} train demos".format(
len(self.train_demos)))
self.train_demos = self.train_demos[:args.episodes]
self.val_demos = utils.load_demos(demos_path_valid)
if args.val_episodes > len(self.val_demos):
logger.info(
'Using all the available {} demos to evaluate valid. accuracy'
.format(len(self.val_demos)))
self.val_demos = self.val_demos[:self.args.val_episodes]
observation_space = self.env.observation_space
action_space = self.env.action_space
self.obss_preprocessor = utils.select_obss_preprocessor(
args.model, observation_space,
getattr(self.args, 'pretrained_model', None))
# Define actor-critic model
self.acmodel = utils.load_model(args.model, raise_not_found=False)
if self.acmodel is None:
if getattr(self.args, 'pretrained_model', None):
self.acmodel = utils.load_model(args.pretrained_model,
raise_not_found=True)
else:
logger.info('Creating new model')
self.acmodel = ACModel(self.obss_preprocessor.obs_space,
action_space, args.image_dim,
args.memory_dim, args.instr_dim,
not self.args.no_instr,
self.args.instr_arch,
not self.args.no_mem, self.args.arch)
if self.obss_preprocessor.vocab is not None:
self.obss_preprocessor.vocab.save()
utils.save_model(self.acmodel, args.model)
self.acmodel.train()
if torch.cuda.is_available():
self.acmodel.cuda()
self.optimizer = torch.optim.Adam(self.acmodel.parameters(),
self.args.lr,
eps=self.args.optim_eps)
self.scheduler = torch.optim.lr_scheduler.StepLR(self.optimizer,
step_size=100,
gamma=0.9)
self.device = torch.device(
"cuda" if torch.cuda.is_available() else "cpu")
def partial_rgb_train(env):
return RGBImgPartialObsWrapper(env, tile_size=6)
