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dreamer.py
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dreamer.py
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import argparse
import collections
import functools
import json
import os
import pathlib
import sys
import time
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'
os.environ['MUJOCO_GL'] = 'egl'
import numpy as np
import tensorflow as tf
from tensorflow.keras.mixed_precision import experimental as prec
tf.get_logger().setLevel('ERROR')
from tensorflow_probability import distributions as tfd
sys.path.append(str(pathlib.Path(__file__).parent))
import models
import tools
import wrappers
def define_config():
config = tools.AttrDict()
# General.
config.logdir = pathlib.Path('.')
config.seed = 0
config.steps = 5e6
config.eval_every = 1e4
config.log_every = 1e3
config.log_scalars = True
config.log_images = True
config.gpu_growth = True
config.precision = 16
# Environment.
config.task = 'dmc_walker_walk'
config.envs = 1
config.parallel = 'none'
config.action_repeat = 2
config.time_limit = 1000
config.prefill = 5000
config.eval_noise = 0.0
config.clip_rewards = 'none'
# Model.
config.deter_size = 200
config.stoch_size = 30
config.num_units = 400
config.dense_act = 'elu'
config.cnn_act = 'relu'
config.cnn_depth = 32
config.pcont = False
config.free_nats = 3.0
config.kl_scale = 1.0
config.pcont_scale = 10.0
config.weight_decay = 0.0
config.weight_decay_pattern = r'.*'
# Training.
config.batch_size = 50
config.batch_length = 50
config.train_every = 1000
config.train_steps = 100
config.pretrain = 100
config.model_lr = 6e-4
config.value_lr = 8e-5
config.actor_lr = 8e-5
config.grad_clip = 100.0
config.dataset_balance = False
# Behavior.
config.discount = 0.99
config.disclam = 0.95
config.horizon = 15
config.action_dist = 'tanh_normal'
config.action_init_std = 5.0
config.expl = 'additive_gaussian'
config.expl_amount = 0.3
config.expl_decay = 0.0
config.expl_min = 0.0
return config
class Dreamer(tools.Module):
def __init__(self, config, datadir, actspace, writer):
self._c = config
self._actspace = actspace
self._actdim = actspace.n if hasattr(actspace, 'n') else actspace.shape[0]
self._writer = writer
self._random = np.random.RandomState(config.seed)
with tf.device('cpu:0'):
self._step = tf.Variable(count_steps(datadir, config), dtype=tf.int64)
self._should_pretrain = tools.Once()
self._should_train = tools.Every(config.train_every)
self._should_log = tools.Every(config.log_every)
self._last_log = None
self._last_time = time.time()
self._metrics = collections.defaultdict(tf.metrics.Mean)
self._metrics['expl_amount'] # Create variable for checkpoint.
self._float = prec.global_policy().compute_dtype
self._strategy = tf.distribute.MirroredStrategy()
with self._strategy.scope():
self._dataset = iter(self._strategy.experimental_distribute_dataset(
load_dataset(datadir, self._c)))
self._build_model()
def __call__(self, obs, reset, state=None, training=True):
step = self._step.numpy().item()
tf.summary.experimental.set_step(step)
if state is not None and reset.any():
mask = tf.cast(1 - reset, self._float)[:, None]
state = tf.nest.map_structure(lambda x: x * mask, state)
if self._should_train(step):
log = self._should_log(step)
n = self._c.pretrain if self._should_pretrain() else self._c.train_steps
print(f'Training for {n} steps.')
with self._strategy.scope():
for train_step in range(n):
log_images = self._c.log_images and log and train_step == 0
self.train(next(self._dataset), log_images)
if log:
self._write_summaries()
action, state = self.policy(obs, state, training)
if training:
self._step.assign_add(len(reset) * self._c.action_repeat)
return action, state
@tf.function
def policy(self, obs, state, training):
if state is None:
latent = self._dynamics.initial(len(obs['image']))
action = tf.zeros((len(obs['image']), self._actdim), self._float)
else:
latent, action = state
embed = self._encode(preprocess(obs, self._c))
latent, _ = self._dynamics.obs_step(latent, action, embed)
feat = self._dynamics.get_feat(latent)
if training:
action = self._actor(feat).sample()
else:
action = self._actor(feat).mode()
action = self._exploration(action, training)
state = (latent, action)
return action, state
def load(self, filename):
super().load(filename)
self._should_pretrain()
@tf.function()
def train(self, data, log_images=False):
self._strategy.experimental_run_v2(self._train, args=(data, log_images))
def _train(self, data, log_images):
with tf.GradientTape() as model_tape:
embed = self._encode(data)
post, prior = self._dynamics.observe(embed, data['action'])
feat = self._dynamics.get_feat(post)
image_pred = self._decode(feat)
reward_pred = self._reward(feat)
likes = tools.AttrDict()
likes.image = tf.reduce_mean(image_pred.log_prob(data['image']))
likes.reward = tf.reduce_mean(reward_pred.log_prob(data['reward']))
if self._c.pcont:
pcont_pred = self._pcont(feat)
pcont_target = self._c.discount * data['discount']
likes.pcont = tf.reduce_mean(pcont_pred.log_prob(pcont_target))
likes.pcont *= self._c.pcont_scale
prior_dist = self._dynamics.get_dist(prior)
post_dist = self._dynamics.get_dist(post)
div = tf.reduce_mean(tfd.kl_divergence(post_dist, prior_dist))
div = tf.maximum(div, self._c.free_nats)
model_loss = self._c.kl_scale * div - sum(likes.values())
model_loss /= float(self._strategy.num_replicas_in_sync)
with tf.GradientTape() as actor_tape:
imag_feat = self._imagine_ahead(post)
reward = self._reward(imag_feat).mode()
if self._c.pcont:
pcont = self._pcont(imag_feat).mean()
else:
pcont = self._c.discount * tf.ones_like(reward)
value = self._value(imag_feat).mode()
returns = tools.lambda_return(
reward[:-1], value[:-1], pcont[:-1],
bootstrap=value[-1], lambda_=self._c.disclam, axis=0)
discount = tf.stop_gradient(tf.math.cumprod(tf.concat(
[tf.ones_like(pcont[:1]), pcont[:-2]], 0), 0))
actor_loss = -tf.reduce_mean(discount * returns)
actor_loss /= float(self._strategy.num_replicas_in_sync)
with tf.GradientTape() as value_tape:
value_pred = self._value(imag_feat)[:-1]
target = tf.stop_gradient(returns)
value_loss = -tf.reduce_mean(discount * value_pred.log_prob(target))
value_loss /= float(self._strategy.num_replicas_in_sync)
model_norm = self._model_opt(model_tape, model_loss)
actor_norm = self._actor_opt(actor_tape, actor_loss)
value_norm = self._value_opt(value_tape, value_loss)
if tf.distribute.get_replica_context().replica_id_in_sync_group == 0:
if self._c.log_scalars:
self._scalar_summaries(
data, feat, prior_dist, post_dist, likes, div,
model_loss, value_loss, actor_loss, model_norm, value_norm,
actor_norm)
if tf.equal(log_images, True):
self._image_summaries(data, embed, image_pred)
def _build_model(self):
acts = dict(
elu=tf.nn.elu, relu=tf.nn.relu, swish=tf.nn.swish,
leaky_relu=tf.nn.leaky_relu)
cnn_act = acts[self._c.cnn_act]
act = acts[self._c.dense_act]
self._encode = models.ConvEncoder(self._c.cnn_depth, cnn_act)
self._dynamics = models.RSSM(
self._c.stoch_size, self._c.deter_size, self._c.deter_size)
self._decode = models.ConvDecoder(self._c.cnn_depth, cnn_act)
self._reward = models.DenseDecoder((), 2, self._c.num_units, act=act)
if self._c.pcont:
self._pcont = models.DenseDecoder(
(), 3, self._c.num_units, 'binary', act=act)
self._value = models.DenseDecoder((), 3, self._c.num_units, act=act)
self._actor = models.ActionDecoder(
self._actdim, 4, self._c.num_units, self._c.action_dist,
init_std=self._c.action_init_std, act=act)
model_modules = [self._encode, self._dynamics, self._decode, self._reward]
if self._c.pcont:
model_modules.append(self._pcont)
Optimizer = functools.partial(
tools.Adam, wd=self._c.weight_decay, clip=self._c.grad_clip,
wdpattern=self._c.weight_decay_pattern)
self._model_opt = Optimizer('model', model_modules, self._c.model_lr)
self._value_opt = Optimizer('value', [self._value], self._c.value_lr)
self._actor_opt = Optimizer('actor', [self._actor], self._c.actor_lr)
# Do a train step to initialize all variables, including optimizer
# statistics. Ideally, we would use batch size zero, but that doesn't work
# in multi-GPU mode.
self.train(next(self._dataset))
def _exploration(self, action, training):
if training:
amount = self._c.expl_amount
if self._c.expl_decay:
amount *= 0.5 ** (tf.cast(self._step, tf.float32) / self._c.expl_decay)
if self._c.expl_min:
amount = tf.maximum(self._c.expl_min, amount)
self._metrics['expl_amount'].update_state(amount)
elif self._c.eval_noise:
amount = self._c.eval_noise
else:
return action
if self._c.expl == 'additive_gaussian':
return tf.clip_by_value(tfd.Normal(action, amount).sample(), -1, 1)
if self._c.expl == 'completely_random':
return tf.random.uniform(action.shape, -1, 1)
if self._c.expl == 'epsilon_greedy':
indices = tfd.Categorical(0 * action).sample()
return tf.where(
tf.random.uniform(action.shape[:1], 0, 1) < amount,
tf.one_hot(indices, action.shape[-1], dtype=self._float),
action)
raise NotImplementedError(self._c.expl)
def _imagine_ahead(self, post):
if self._c.pcont: # Last step could be terminal.
post = {k: v[:, :-1] for k, v in post.items()}
flatten = lambda x: tf.reshape(x, [-1] + list(x.shape[2:]))
start = {k: flatten(v) for k, v in post.items()}
policy = lambda state: self._actor(
tf.stop_gradient(self._dynamics.get_feat(state))).sample()
states = tools.static_scan(
lambda prev, _: self._dynamics.img_step(prev, policy(prev)),
tf.range(self._c.horizon), start)
imag_feat = self._dynamics.get_feat(states)
return imag_feat
def _scalar_summaries(
self, data, feat, prior_dist, post_dist, likes, div,
model_loss, value_loss, actor_loss, model_norm, value_norm,
actor_norm):
self._metrics['model_grad_norm'].update_state(model_norm)
self._metrics['value_grad_norm'].update_state(value_norm)
self._metrics['actor_grad_norm'].update_state(actor_norm)
self._metrics['prior_ent'].update_state(prior_dist.entropy())
self._metrics['post_ent'].update_state(post_dist.entropy())
for name, logprob in likes.items():
self._metrics[name + '_loss'].update_state(-logprob)
self._metrics['div'].update_state(div)
self._metrics['model_loss'].update_state(model_loss)
self._metrics['value_loss'].update_state(value_loss)
self._metrics['actor_loss'].update_state(actor_loss)
self._metrics['action_ent'].update_state(self._actor(feat).entropy())
def _image_summaries(self, data, embed, image_pred):
truth = data['image'][:6] + 0.5
recon = image_pred.mode()[:6]
init, _ = self._dynamics.observe(embed[:6, :5], data['action'][:6, :5])
init = {k: v[:, -1] for k, v in init.items()}
prior = self._dynamics.imagine(data['action'][:6, 5:], init)
openl = self._decode(self._dynamics.get_feat(prior)).mode()
model = tf.concat([recon[:, :5] + 0.5, openl + 0.5], 1)
error = (model - truth + 1) / 2
openl = tf.concat([truth, model, error], 2)
tools.graph_summary(
self._writer, tools.video_summary, 'agent/openl', openl)
def _write_summaries(self):
step = int(self._step.numpy())
metrics = [(k, float(v.result())) for k, v in self._metrics.items()]
if self._last_log is not None:
duration = time.time() - self._last_time
self._last_time += duration
metrics.append(('fps', (step - self._last_log) / duration))
self._last_log = step
[m.reset_states() for m in self._metrics.values()]
with (self._c.logdir / 'metrics.jsonl').open('a') as f:
f.write(json.dumps({'step': step, **dict(metrics)}) + '\n')
[tf.summary.scalar('agent/' + k, m) for k, m in metrics]
print(f'[{step}]', ' / '.join(f'{k} {v:.1f}' for k, v in metrics))
self._writer.flush()
def preprocess(obs, config):
dtype = prec.global_policy().compute_dtype
obs = obs.copy()
with tf.device('cpu:0'):
obs['image'] = tf.cast(obs['image'], dtype) / 255.0 - 0.5
clip_rewards = dict(none=lambda x: x, tanh=tf.tanh)[config.clip_rewards]
obs['reward'] = clip_rewards(obs['reward'])
return obs
def count_steps(datadir, config):
return tools.count_episodes(datadir)[1] * config.action_repeat
def load_dataset(directory, config):
episode = next(tools.load_episodes(directory, 1))
types = {k: v.dtype for k, v in episode.items()}
shapes = {k: (None,) + v.shape[1:] for k, v in episode.items()}
generator = lambda: tools.load_episodes(
directory, config.train_steps, config.batch_length,
config.dataset_balance)
dataset = tf.data.Dataset.from_generator(generator, types, shapes)
dataset = dataset.batch(config.batch_size, drop_remainder=True)
dataset = dataset.map(functools.partial(preprocess, config=config))
dataset = dataset.prefetch(10)
return dataset
def summarize_episode(episode, config, datadir, writer, prefix):
episodes, steps = tools.count_episodes(datadir)
length = (len(episode['reward']) - 1) * config.action_repeat
ret = episode['reward'].sum()
print(f'{prefix.title()} episode of length {length} with return {ret:.1f}.')
metrics = [
(f'{prefix}/return', float(episode['reward'].sum())),
(f'{prefix}/length', len(episode['reward']) - 1),
(f'episodes', episodes)]
step = count_steps(datadir, config)
with (config.logdir / 'metrics.jsonl').open('a') as f:
f.write(json.dumps(dict([('step', step)] + metrics)) + '\n')
with writer.as_default(): # Env might run in a different thread.
tf.summary.experimental.set_step(step)
[tf.summary.scalar('sim/' + k, v) for k, v in metrics]
if prefix == 'test':
tools.video_summary(f'sim/{prefix}/video', episode['image'][None])
def make_env(config, writer, prefix, datadir, store):
suite, task = config.task.split('_', 1)
if suite == 'dmc':
env = wrappers.DeepMindControl(task)
env = wrappers.ActionRepeat(env, config.action_repeat)
env = wrappers.NormalizeActions(env)
elif suite == 'atari':
env = wrappers.Atari(
task, config.action_repeat, (64, 64), grayscale=False,
life_done=True, sticky_actions=True)
env = wrappers.OneHotAction(env)
else:
raise NotImplementedError(suite)
env = wrappers.TimeLimit(env, config.time_limit / config.action_repeat)
callbacks = []
if store:
callbacks.append(lambda ep: tools.save_episodes(datadir, [ep]))
callbacks.append(
lambda ep: summarize_episode(ep, config, datadir, writer, prefix))
env = wrappers.Collect(env, callbacks, config.precision)
env = wrappers.RewardObs(env)
return env
def main(config):
if config.gpu_growth:
for gpu in tf.config.experimental.list_physical_devices('GPU'):
tf.config.experimental.set_memory_growth(gpu, True)
assert config.precision in (16, 32), config.precision
if config.precision == 16:
prec.set_policy(prec.Policy('mixed_float16'))
config.steps = int(config.steps)
config.logdir.mkdir(parents=True, exist_ok=True)
print('Logdir', config.logdir)
# Create environments.
datadir = config.logdir / 'episodes'
writer = tf.summary.create_file_writer(
str(config.logdir), max_queue=1000, flush_millis=20000)
writer.set_as_default()
train_envs = [wrappers.Async(lambda: make_env(
config, writer, 'train', datadir, store=True), config.parallel)
for _ in range(config.envs)]
test_envs = [wrappers.Async(lambda: make_env(
config, writer, 'test', datadir, store=False), config.parallel)
for _ in range(config.envs)]
actspace = train_envs[0].action_space
# Prefill dataset with random episodes.
step = count_steps(datadir, config)
prefill = max(0, config.prefill - step)
print(f'Prefill dataset with {prefill} steps.')
random_agent = lambda o, d, _: ([actspace.sample() for _ in d], None)
tools.simulate(random_agent, train_envs, prefill / config.action_repeat)
writer.flush()
# Train and regularly evaluate the agent.
step = count_steps(datadir, config)
print(f'Simulating agent for {config.steps-step} steps.')
agent = Dreamer(config, datadir, actspace, writer)
if (config.logdir / 'variables.pkl').exists():
print('Load checkpoint.')
agent.load(config.logdir / 'variables.pkl')
state = None
while step < config.steps:
print('Start evaluation.')
tools.simulate(
functools.partial(agent, training=False), test_envs, episodes=1)
writer.flush()
print('Start collection.')
steps = config.eval_every // config.action_repeat
state = tools.simulate(agent, train_envs, steps, state=state)
step = count_steps(datadir, config)
agent.save(config.logdir / 'variables.pkl')
for env in train_envs + test_envs:
env.close()
if __name__ == '__main__':
try:
import colored_traceback
colored_traceback.add_hook()
except ImportError:
pass
parser = argparse.ArgumentParser()
for key, value in define_config().items():
parser.add_argument(f'--{key}', type=tools.args_type(value), default=value)
main(parser.parse_args())