dreamer.py

dreamer.py

import argparse
import collections
import functools
import gc
import os
import pathlib
import resource
import sys
import warnings

warnings.filterwarnings('ignore', '.*box bound precision lowered.*')

# 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.load_config = False
  config.ckpt = True
  config.logdir = pathlib.Path('.')
  config.ckptdir = pathlib.Path('.')
  config.traindir = None
  config.evaldir = None
  config.seed = 0
  config.steps = 1e7
  config.eval_every = 1e4
  config.log_every = 1e4
  config.backup_every = 1e3
  config.gpu_growth = True
  config.precision = 16
  config.debug = False
  config.expl_gifs = False
  config.compress_dataset = False
  config.gc = False
  config.deleps = False

  # Environment.
  config.task = 'dmc_walker_walk'
  config.size = (64, 64)
  config.parallel = 'none'
  config.envs = 1
  config.action_repeat = 2
  config.time_limit = 1000
  config.prefill = 5000
  config.eval_noise = 0.0
  config.clip_rewards = 'identity'
  config.atari_grayscale = False
  config.atari_lifes = False
  config.atari_fire_start = False
  config.dmlab_path = None

  # Model.
  config.dynamics = 'rssm'
  config.grad_heads = ('image', 'reward')
  config.deter_size = 200
  config.stoch_size = 50
  config.stoch_depth = 10
  config.dynamics_deep = False
  config.units = 400
  config.reward_layers = 2
  config.discount_layers = 3
  config.value_layers = 3
  config.actor_layers = 4
  config.act = 'elu'
  config.cnn_depth = 32
  config.encoder_kernels = (4, 4, 4, 4)
  config.decoder_kernels = (5, 5, 6, 6)
  config.decoder_thin = True
  config.cnn_embed_size = 0
  config.kl_scale = 1.0
  config.free_nats = 3.0
  config.kl_balance = 0.8
  config.pred_discount = False
  config.discount_rare_bonus = 0.0
  config.discount_scale = 10.0
  config.reward_scale = 2.0
  config.weight_decay = 0.0
  config.proprio = False

  # Training.
  config.batch_size = 50
  config.batch_length = 50
  config.train_every = 500
  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.opt_eps = 1e-5
  config.value_grad_clip = 100.0
  config.actor_grad_clip = 100.0
  config.dataset_size = 0
  config.dropout_embed = 0.0
  config.dropout_feat = 0.0
  config.dropout_imag = 0.0
  config.oversample_ends = False
  config.slow_value_target = False
  config.slow_actor_target = False
  config.slow_target_update = 0
  config.slow_target_soft = False
  config.slow_target_moving_weight = 0.1

  # Behavior.
  config.discount = 0.99
  config.discount_lambda = 0.95
  config.imag_horizon = 15
  config.actor_dist = 'tanh_normal'
  config.actor_disc = 5
  config.actor_entropy = 0.0
  config.actor_init_std = 5.0
  config.expl = 'additive_gaussian'
  config.expl_amount = 0.3
  config.eval_state_mean = False
  config.reuse_task_behavior = False
  config.task_expl_reward_weights = 0.5

  # Exploration.
  config.expl_behavior = 'greedy'
  config.expl_until = 0
  config.disag_target = 'stoch'  # embed
  config.disag_scale = 5.0
  config.disag_log = True
  config.disag_samples = 10
  config.disag_models = 10
  config.disag_offset = 1
  config.disag_layers = 4
  config.vim_layers = 4
  config.disag_units = 400
  config.disag_project = 0  # 100
  config.disag_noise_scale = 10.0
  config.disag_save_memory = True
  config.empow_scale = 1.0
  config.empow_entropy = 'auto'
  config.empow_marginal = 'sequences'
  config.empow_sequences = 5  # 3
  config.empow_samples = 5  # 3
  config.temperature = 1.0
  config.empow_horizon = 1
  config.reuse_actor = False

  return config


class Dreamer(tools.Module):

  def __init__(self, config, logger, dataset):
    self._config = config
    self._logger = logger
    self._float = prec.global_policy().compute_dtype
    self._should_log = tools.Every(config.log_every)
    self._should_train = tools.Every(config.train_every)
    self._should_pretrain = tools.Once()
    self._should_expl = tools.Until(int(config.expl_until / config.action_repeat))
    self._metrics = collections.defaultdict(tf.metrics.Mean)
    with tf.device('cpu:0'):
      self._step = tf.Variable(count_steps(config.traindir), dtype=tf.int64)
    # self._strategy = tf.distribute.MirroredStrategy()
    # with self._strategy.scope():
    # self._dataset = self._strategy.experimental_distribute_dataset(dataset)
    self._dataset = iter(dataset)
    self._world_model = models.WorldModel(config)
    self._behavior = models.ImagBehavior(config, self._world_model)
    if not config.reuse_task_behavior:
      behavior = models.ImagBehavior(config, self._world_model)
    else:
      behavior = self._behavior
    self._expl_behavior = dict(
        greedy=lambda: self._behavior,
        random=lambda: models.RandomBehavior(config),
        disag=lambda: models.DisagBehavior(config, self._world_model, behavior),
        disag_info=lambda: models.DisagInfoBehavior(config, self._world_model, behavior),
        disag_noise=lambda: models.DisagNoiseBehavior(config, self._world_model, behavior),
        empow_open=lambda: models.EmpowOpenBehavior(config, self._world_model, behavior),
        empow_action=lambda: models.EmpowActionBehavior(config, self._world_model, behavior),
        empow_state=lambda: models.EmpowStateBehavior(config, self._world_model, behavior),
        empow_step_state=lambda: models.EmpowStepStateBehavior(config, self._world_model, behavior),
        empow_step_action=lambda: models.EmpowStepActionBehavior(config, self._world_model, behavior),
        empow_vim=lambda: models.EmpowVIMBehavior(config, self._world_model, behavior),
    )[config.expl_behavior]()
    # Train step to initialize variables including optimizer statistics.
    self.train(next(self._dataset))

  def __call__(self, obs, reset, state=None, training=True):
    step = self._step.numpy().item()
    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 training and self._should_train(step):
      # with self._strategy.scope():
      steps = (
          self._config.pretrain if self._should_pretrain()
          else self._config.train_steps)
      for _ in range(steps):
        self.train(next(self._dataset))
      if self._should_log(step):
        for name, mean in self._metrics.items():
          self._logger.scalar(name, float(mean.result()))
          mean.reset_states()
        openl = self._world_model.video_pred(next(self._dataset))
        self._logger.video('train_openl', openl)
        self._logger.write(fps=True)
    action, state = self._policy(obs, state, training)
    if training:
      self._step.assign_add(len(reset))
      self._logger.step = self._config.action_repeat * self._step.numpy().item()
    return action, state

  @tf.function
  def _policy(self, obs, state, training):
    if state is None:
      batch_size = len(obs['image'])
      latent = self._world_model.dynamics.initial(len(obs['image']))
      action = tf.zeros((batch_size, self._config.num_actions), self._float)
    else:
      latent, action = state
    embed = self._world_model.encoder(self._world_model.preprocess(obs))
    latent, _ = self._world_model.dynamics.obs_step(latent, action, embed)
    if self._config.eval_state_mean:
      latent['stoch'] = latent['mean']
    feat = self._world_model.dynamics.get_feat(latent)
    if not training:
      action = self._behavior.actor(feat).mode()
    elif self._should_expl(self._step):
      action = self._expl_behavior.actor(feat).sample()
    else:
      action = self._behavior.actor(feat).sample()
    action = self._exploration(action, training)
    state = (latent, action)
    return action, state

  def _exploration(self, action, training):
    amount = self._config.expl_amount if training else self._config.eval_noise
    if amount == 0:
      return action
    amount = tf.cast(amount, self._float)
    if self._config.expl == 'additive_gaussian':
      return tf.clip_by_value(tfd.Normal(action, amount).sample(), -1, 1)
    if self._config.expl == 'epsilon_greedy':
      probs = amount / self._config.num_actions + (1 - amount) * action
      return tools.OneHotDist(probs=probs).sample()
    raise NotImplementedError(self._config.expl)

  # @tf.function
  # def train(self, data):
  #   self._strategy.experimental_run_v2(self._train, (data,))

  @tf.function
  def train(self, data):
    metrics = {}
    embed, post, feat, mets = self._world_model.train(data)
    metrics.update(mets)
    reward = lambda f, s, a: self._world_model.heads['reward'](f).mode()
    if not self._config.reuse_task_behavior:
      metrics.update(self._behavior.train(post, reward))
    if self._config.expl_behavior != 'greedy':
      if self._config.reuse_task_behavior:
        mets = self._expl_behavior.train(post, feat, embed, reward)
      else:
        mets = self._expl_behavior.train(post, feat, embed)
      metrics.update({'expl_' + key: value for key, value in mets.items()})
    # if tf.distribute.get_replica_context().replica_id_in_sync_group == 0:
    # if self._config.pred_discount:
    #   metrics.update(self._episode_end_precision_recall(data, feat))
    for name, value in metrics.items():
      self._metrics[name].update_state(value)

  # def _episode_end_precision_recall(self, data, feat):
  #   pred = self._world_model.heads['discount'](feat).mean()
  #   ppos, pneg = pred <= 0.5, pred > 0.5
  #   dpos, dneg = data['discount'] <= 0.5, data['discount'] > 0.5
  #   tp = tf.reduce_sum(tf.cast(tf.logical_and(ppos, dpos), self._float))
  #   tn = tf.reduce_sum(tf.cast(tf.logical_and(pneg, dneg), self._float))
  #   fp = tf.reduce_sum(tf.cast(tf.logical_and(ppos, dneg), self._float))
  #   fn = tf.reduce_sum(tf.cast(tf.logical_and(pneg, dpos), self._float))
  #   tpr, tnr, ppv = tp / (tp + fn), tn / (tn + fp), tp / (tp + fp)
  #   ba, f1 = (tpr + tnr) / 2, 2 * ppv * tpr / (ppv + tpr)
  #   any_ = tf.cast(tf.reduce_any(dpos), self._float)
  #   metrics = dict(
  #       ee_tp=tp, ee_tn=tn, ee_fp=fp, ee_fn=fn, ee_tpr=tpr, ee_tnr=tnr,
  #       ee_ppv=ppv, ee_ba=ba, ee_f1=f1, ee_any=any_)
  #   return {
  #       k: tf.where(tf.math.is_nan(v), tf.zeros_like(v), v)
  #       for k, v in metrics.items()}


def count_steps(directory):
  return sum(int(str(n).split('-')[-1][:-4]) - 1 for n in directory.glob('*.npz'))


def make_dataset(episodes, config):
  example = episodes[next(iter(episodes.keys()))]
  example = tools.decompress_episode(example)
  types = {k: v.dtype for k, v in example.items()}
  shapes = {k: (None,) + v.shape[1:] for k, v in example.items()}
  generator = lambda: tools.sample_episodes(
      episodes, config.batch_length, config.oversample_ends)
  dataset = tf.data.Dataset.from_generator(generator, types, shapes)
  dataset = dataset.batch(config.batch_size, drop_remainder=True)
  dataset = dataset.prefetch(10)
  return dataset


def make_env(config, logger, mode, train_eps, eval_eps):
  suite, task = config.task.split('_', 1)
  if suite == 'dmc' or suite == 'dmctoy':
    env = wrappers.DeepMindControl(task, config.action_repeat, config.size)
    env = wrappers.NormalizeActions(env)
  elif suite == 'atari':
    env = wrappers.Atari(
        task, config.action_repeat, config.size,
        grayscale=config.atari_grayscale, sticky_actions=True,
        life_done=config.atari_lifes and (mode == 'train'))
    env = wrappers.OneHotAction(env)
  elif suite == 'dmlab':
    args = task, config.action_repeat, config.size
    env = wrappers.DeepMindLab(*args, path=config.dmlab_path)
    env = wrappers.OneHotAction(env)
  elif suite == 'mc':
    args = task, mode, config.size, config.action_repeat
    env = wrappers.Minecraft(*args)
    env = wrappers.OneHotAction(env)
  else:
    raise NotImplementedError(suite)
  env = wrappers.TimeLimit(env, config.time_limit)
  callbacks = [functools.partial(
      process_episode, config, logger, mode, train_eps, eval_eps)]
  env = wrappers.Collect(env, callbacks, config.precision)
  env = wrappers.RewardObs(env)
  return env


def process_episode(config, logger, mode, train_eps, eval_eps, episode):
  directory = dict(train=config.traindir, eval=config.evaldir)[mode]
  cache = dict(train=train_eps, eval=eval_eps)[mode]
  filename = tools.save_episodes(directory, [episode])[0]
  length = len(episode['reward']) - 1
  score = float(episode['reward'].astype(np.float32).sum())
  video = episode['image']
  if mode == 'eval':
    if config.deleps:
      for key, value in list(cache.items()):
        del cache[key]
        del value
    cache.clear()
  if mode == 'train' and config.dataset_size:
    total = 0
    for key, ep in reversed(sorted(cache.items(), key=lambda x: x[0])):
      if total <= config.dataset_size - length:
        total += len(ep['reward']) - 1
      else:
        del cache[key]
        if config.deleps:
          del ep
    logger.scalar('dataset_size', total + length)
  if config.compress_dataset:
    episode = tools.compress_episode(episode, (np.uint8,))
  cache[str(filename)] = episode
  print(f'{mode.title()} episode has {length} steps and return {score:.1f}.')
  logger.scalar(f'{mode}_return', score)
  logger.scalar(f'{mode}_length', length)
  logger.scalar(f'{mode}_episodes', len(cache))
  if mode == 'eval' or config.expl_gifs:
    logger.video(f'{mode}_policy', video[None])
  logger.write()

def main(config):
  if config.load_config:
    from configs import default_configs
    suite, _ = config.task.split('_', 1)
    default = default_configs[suite]
    config = vars(config)
    for key, value in default.items():
      config[key] = value
    config = tools.AttrDict(config)

  print(config)

  config.traindir = config.traindir or config.logdir / 'train_eps'
  config.evaldir = config.evaldir or config.logdir / 'eval_eps'
  config.steps //= config.action_repeat
  config.eval_every //= config.action_repeat
  config.log_every //= config.action_repeat
  config.backup_every //= config.action_repeat
  config.time_limit //= config.action_repeat
  config.act = getattr(tf.nn, config.act)
  if config.empow_entropy == 'auto':
    options = dict(
        tanh_normal='sample', onehot='sample', discretized='analytic')
    config.empow_entropy = options[config.actor_dist]

  if config.debug:
    tf.config.experimental_run_functions_eagerly(True)
  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'))
  print('Logdir', config.logdir)
  config.logdir.mkdir(parents=True, exist_ok=True)
  step = count_steps(config.traindir)
  logger = tools.Logger(config.logdir, config.action_repeat * step)

  print('Create envs.')
  train_eps = tools.load_episodes(config.traindir, limit=config.dataset_size)
  eval_eps = tools.load_episodes(config.evaldir, limit=1)
  make1 = lambda mode: make_env(config, logger, mode, train_eps, eval_eps)
  make2 = lambda mode: wrappers.Async(lambda: make1(mode), config.parallel)
  train_envs = [make2('train') for _ in range(config.envs)]
  eval_envs = [make2('eval') for _ in range(config.envs)]
  acts = train_envs[0].action_space
  config.num_actions = acts.n if hasattr(acts, 'n') else acts.shape[0]

  prefill = max(0, config.prefill - count_steps(config.traindir))
  print(f'Prefill dataset ({prefill} steps).')
  random_agent = lambda o, d, s: ([acts.sample() for _ in d], s)
  tools.simulate(random_agent, train_envs, prefill)
  tools.simulate(random_agent, eval_envs, episodes=1)
  logger.step = config.action_repeat * count_steps(config.traindir)

  print('Simulate agent.')
  train_dataset = make_dataset(train_eps, config)
  eval_dataset = iter(make_dataset(eval_eps, config))
  agent = Dreamer(config, logger, train_dataset)
  if (config.logdir / 'variables.pkl').exists():
    try:
      agent.load(config.logdir / 'variables.pkl')
    except:
      agent.load(config.logdir / 'variables-backup.pkl')
    agent._should_pretrain._once = False
  state = None
  should_backup = tools.Every(config.backup_every)
  while agent._step.numpy().item() < config.steps:
    print('Process statistics.')
    if config.gc:
      gc.collect()
    logger.scalar('python_objects', len(gc.get_objects()))
    with open('/proc/self/statm') as f:
      ram = int(f.read().split()[1]) * resource.getpagesize() / 1024 / 1024 / 1024
    logger.scalar('ram', ram)
    logger.write()
    print('Start evaluation.')
    video_pred = agent._world_model.video_pred(next(eval_dataset))
    logger.video('eval_openl', video_pred)
    eval_policy = functools.partial(agent, training=False)
    tools.simulate(eval_policy, eval_envs, episodes=1)
    print('Start training.')
    state = tools.simulate(agent, train_envs, config.eval_every, state=state)

    if config.ckpt:
      agent.save(config.ckptdir / 'variables.pkl')
    agent.save(config.logdir / 'variables.pkl')
    if should_backup(agent._step.numpy().item()):
      if config.ckpt:
        agent.save(config.ckptdir / 'variables-backup.pkl')
      agent.save(config.logdir / 'variables-backup.pkl')
  for env in train_envs + eval_envs:
    env.close()


if __name__ == '__main__':
  print(os.getpid())
  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())

models.py

class EmpowStepActionBehavior(tools.Module): # I(S';A|S), action space

  def __init__(self, config, world_model, behavior):
    self._config = config
    self._world_model = world_model
    self._behavior = behavior
    self.actor = self._behavior.actor
    self._tau = config.temperature # temperature
    
    self._plan_ss = networks.ActionHead(
        config.num_actions, config.actor_layers, config.units, config.act,
        config.actor_dist, config.actor_init_std, config.actor_disc) # p(a_0|s, s')
    if config.reuse_actor:
      self._source = self._behavior.actor
    else:
      self._source = networks.ActionHead(
          config.num_actions, config.actor_layers, config.units, config.act,
          config.actor_dist, config.actor_init_std, config.actor_disc)
    
    self._offset = networks.DenseHead(
      [], config.actor_layers, config.units, config.act)
    
    self._plan_opt = tools.Adam(
        'plan', config.actor_lr, config.opt_eps, config.grad_clip,
        config.weight_decay)
    self._source_opt = tools.Adam(
        'source', config.actor_lr, config.opt_eps, config.grad_clip,
        config.weight_decay) # for both source and offset

  def train(self, start, feat, embed, task_reward=None):
    metrics = {}
    metrics.update(self._behavior.train(start, lambda f, s, a: self._intrinsic_reward(f, s, a, task_reward)))
    metrics.update(self._train_step(start))
    return metrics
  
  def _intrinsic_reward(self, feat, state, action, task_reward=None):
    reward = self._tau * self._offset(feat).mode()
    if task_reward:
      t_reward = task_reward(feat, 0, 0)
      reward = dict(expl=reward, task=t_reward)
    return reward

  def _imagine(self, start): # don't use this for self._behavior.train
    flatten = lambda x: tf.reshape(x, [-1] + list(x.shape[2:]))
    start = {k: flatten(v) for k, v in start.items()}

    def step(prev, _):
      state, _, _ = prev
      feat = self._world_model.dynamics.get_feat(state)
      if self._config.dropout_imag:
        feat = tf.nn.dropout(feat, self._config.dropout_imag)
      action = self._source(tf.stop_gradient(feat)).sample() # source instead of actor
      state = self._world_model.dynamics.img_step(state, action)
      return state, feat, action

    range_ = tf.range(self._config.imag_horizon)
    feat = 0 * self._world_model.dynamics.get_feat(start)
    action = 0 * self._source(feat).mean()
    states, feats, actions = tools.static_scan(step, range_, (start, feat, action))
    return feats, states, actions

  def _train_step(self, start):
    feat, _, action = self._imagine(start)
    ss_feat = tf.concat([feat[:-1], feat[1:]], axis=-1) # time-1, batch, features
    action = tf.stop_gradient(tf.cast(action, tf.float32))

    metrics = {}
    with tf.GradientTape() as tape:
      plan_pred = self._plan_ss(ss_feat, dtype=tf.float32)
      log_plan_prob = plan_pred.log_prob(action[:-1])
      loss = -tf.reduce_mean(log_plan_prob)
        
    norm = self._plan_opt(tape, loss, [self._plan_ss])
    metrics['plan_grad_norm'] = norm
    metrics['plan_loss'] = loss
    if self._config.precision == 16:
      metrics['plan_dynamics_loss_scale'] = self._plan_opt.loss_scale

    with tf.GradientTape() as tape:
      offset = self._offset(feat[:-1], dtype=tf.float32).mode()
      action_pred = self._source(feat[:-1], dtype=tf.float32)
      log_action_prob = action_pred.log_prob(action[:-1])
      r_sa = log_action_prob + offset
      loss = tf.reduce_mean(tf.square(1./self._tau * tf.stop_gradient(log_plan_prob) - r_sa))
    norm = self._source_opt(tape, loss, [self._source, self._offset])
    metrics['source_grad_norm'] = norm
    metrics['source_loss'] = loss
    if self._config.precision == 16:
      metrics['source_loss_scale'] = self._source_opt.loss_scale
    return metrics

class EmpowVIMBehavior(tools.Module): # I(S^T;A|S), action space, n-step

  def __init__(self, config, world_model, behavior):
    self._config = config
    self._world_model = world_model
    self._behavior = behavior
    self.actor = self._behavior.actor
    self._tau = config.temperature # temperature
    
    self._plan = networks.ActionHead(
        config.num_actions, config.actor_layers, config.units, config.act,
        config.actor_dist, config.actor_init_std, config.actor_disc) # p(a_1:t|s, s')
    if config.reuse_actor:
      self._source = self._behavior.actor
    else:
      self._source = networks.ActionHead(
          config.num_actions, config.actor_layers, config.units, config.act,
          config.actor_dist, config.actor_init_std, config.actor_disc) # p(a_1:t|s)
    
    self._offset = networks.DenseHead(
      [], config.actor_layers, config.units, config.act)
    
    self._plan_opt = tools.Adam(
        'plan', config.actor_lr, config.opt_eps, config.grad_clip,
        config.weight_decay)
    self._source_opt = tools.Adam(
        'source', config.actor_lr, config.opt_eps, config.grad_clip,
        config.weight_decay) # for both source and offset

  def train(self, start, feat, embed, task_reward=None):
    metrics = {}
    metrics.update(self._behavior.train(start, lambda f, s, a: self._intrinsic_reward(f, s, a, task_reward)))
    metrics.update(self._train_vim(start))
    return metrics
  
  def _intrinsic_reward(self, feat, state, action, task_reward=None):
    reward = self._tau * self._offset(feat).mode()
    if task_reward:
      t_reward = task_reward(feat, 0, 0)
      reward = dict(expl=reward, task=t_reward)
    return reward

  def _imagine(self, start, actions):
    def step(prev, action):
      state, _ = prev
      feat = self._world_model.dynamics.get_feat(state)
      if self._config.dropout_imag:
        feat = tf.nn.dropout(feat, self._config.dropout_imag)
      action = tf.cast(action, feat.dtype)
      state = self._world_model.dynamics.img_step(state, action)
      return state, feat
    feat = self._world_model.dynamics.get_feat(start)
    states, feats = tools.static_scan(step, actions, (start, feat))
    return feats[-1]

  def _train_vim(self, start):
    flatten = lambda x: tf.reshape(x, [-1] + list(x.shape[2:]))
    start = {k: flatten(v) for k, v in start.items()}
    feat = self._world_model.dynamics.get_feat(start)
    metrics = {}

    # Get the plan
    action_pred, action_list = self._source(feat, dtype=tf.float32, horizon=self._config.empow_horizon)
    # Execute it in the model and retreive the last state
    last_feat = self._imagine(start, tf.stack(action_list, axis=0))

    with tf.GradientTape() as tape:
      plan_pred, _ = self._plan(tf.concat([feat, last_feat], axis=-1), dtype=tf.float32, horizon=self._config.empow_horizon)
      log_plan_prob = [pp.log_prob(tf.stop_gradient(a)) for a, pp in zip(action_list, plan_pred)]
      log_plan_prob = tf.reduce_mean(tf.stack(log_plan_prob, axis=0), axis=0)
      loss = -tf.reduce_mean(log_plan_prob)
    norm = self._plan_opt(tape, loss, [self._plan])
    metrics['plan_grad_norm'] = norm
    metrics['plan_loss'] = loss
    if self._config.precision == 16:
      metrics['plan_loss_scale'] = self._plan_opt.loss_scale

    with tf.GradientTape() as tape:
      offset = self._offset(feat, dtype=tf.float32).mode()
      log_action_prob = [ap.log_prob(tf.stop_gradient(a)) for a, ap in zip(action_list, action_pred)]
      log_action_prob = tf.reduce_mean(tf.stack(log_action_prob, axis=0), axis=0)
      r_sa = log_action_prob + offset
      loss = tf.reduce_mean(tf.square(1./self._tau * tf.stop_gradient(log_plan_prob) - r_sa))
    norm = self._source_opt(tape, loss, [self._source, self._offset])
    metrics['source_grad_norm'] = norm
    metrics['source_loss'] = loss
    if self._config.precision == 16:
      metrics['source_loss_scale'] = self._source_opt.loss_scale
    return metrics

networks.py

class ActionHead(tools.Module):

  def __init__(
      self, size, layers, units, act=tf.nn.elu, dist='tanh_normal',
      init_std=5.0, min_std=1e-4, mean_scale=5.0, action_disc=5):
    self._size = size
    self._layers = layers
    self._units = units
    self._dist = dist
    self._act = act
    self._min_std = min_std
    self._init_std = init_std
    self._mean_scale = mean_scale
    self._action_disc = action_disc
    self._cell = tfkl.GRUCell(self._units)

  def __call__(self, features, dtype=None, horizon=0):
    x = features
    for index in range(self._layers):
      x = self.get(f'h{index}', tfkl.Dense, self._units, self._act)(x)
    h = x # init hidden state
    act_dist = [self.action(h, dtype)]
    if horizon == 0:
      self.hidden = h
      return act_dist[0]
    act = [act_dist[-1].sample()]
    self.hidden = [h]
    for _ in range(horizon-1):
      h, _ = self._cell(act[-1], [h])
      act_dist.append(self.action(h, dtype))
      act.append(act_dist[-1].sample())
      self.hidden.append(h)
    return act_dist, act

  def action(self, features, dtype):
    raw_init_std = np.log(np.exp(self._init_std) - 1)
    x = features
    if self._dist == 'tanh_normal':
      # https://www.desmos.com/calculator/rcmcf5jwe7
      x = self.get(f'hout', tfkl.Dense, 2 * self._size)(x)
      if dtype:
        x = tf.cast(x, dtype)
      mean, std = tf.split(x, 2, -1)
      mean = self._mean_scale * tf.tanh(mean / self._mean_scale)
      std = tf.nn.softplus(std + raw_init_std) + self._min_std
      dist = tfd.Normal(mean, std)
      dist = tfd.TransformedDistribution(dist, tools.TanhBijector())
      dist = tfd.Independent(dist, 1)
      dist = tools.SampleDist(dist)
    elif self._dist == 'normal':
      x = self.get(f'hout', tfkl.Dense, 2 * self._size)(x)
      if dtype:
        x = tf.cast(x, dtype)
      mean, std = tf.split(x, 2, -1)
      mean = self._mean_scale * tf.tanh(mean / self._mean_scale)
      std = tf.nn.softplus(std + raw_init_std) + self._min_std
      dist = tfd.Normal(mean, std)
      dist = tfd.Independent(dist, 1)
    elif self._dist == 'onehot':
      x = self.get(f'hout', tfkl.Dense, self._size)(x)
      if dtype:
        x = tf.cast(x, dtype)
      dist = tools.OneHotDist(x, dtype=dtype)
    elif self._dist == 'discretized':
      x = self.get(f'hout', tfkl.Dense, self._size * self._action_disc)(x)
      x = tf.reshape(x, x.shape[:-1] + [self._size, self._action_disc])
      if dtype:
        x = tf.cast(x, dtype)
      dist = tfd.Independent(tools.UnifDiscDist(x, dtype=dtype), 1)
    else:
      raise NotImplementedError(dist)
    return dist