def __init__(self, env_str, distinct=1, count=1, seeds=None):
self.distinct = distinct
self.count = count
self.total = self.distinct * self.count
self.seeds = seeds or [random.randint(0, 1e12)
for _ in xrange(self.distinct)]
self.envs = []
for seed in self.seeds:
for _ in xrange(self.count):
env = get_env(env_str)
env.seed(seed)
if hasattr(env, 'last'):
env.last = 100 # for algorithmic envs
self.envs.append(env)
self.dones = [True] * self.total
self.num_episodes_played = 0
one_env = self.get_one()
self.use_action_list = hasattr(one_env.action_space, 'spaces')
self.env_spec = env_spec.EnvSpec(self.get_one())
def __init__(self):
self.batch_size = FLAGS.batch_size
self.replay_batch_size = FLAGS.replay_batch_size
if self.replay_batch_size is None:
self.replay_batch_size = self.batch_size
self.num_samples = FLAGS.num_samples
self.env_str = FLAGS.env
self.env = gym_wrapper.GymWrapper(self.env_str,
distinct=FLAGS.batch_size //
self.num_samples,
count=self.num_samples)
self.eval_env = gym_wrapper.GymWrapper(self.env_str,
distinct=FLAGS.batch_size //
self.num_samples,
count=self.num_samples)
self.env_spec = env_spec.EnvSpec(self.env.get_one())
self.max_step = FLAGS.max_step
self.cutoff_agent = FLAGS.cutoff_agent
self.num_steps = FLAGS.num_steps
self.validation_frequency = FLAGS.validation_frequency
self.target_network_lag = FLAGS.target_network_lag
self.sample_from = FLAGS.sample_from
assert self.sample_from in ['online', 'target']
self.critic_weight = FLAGS.critic_weight
self.objective = FLAGS.objective
self.trust_region_p = FLAGS.trust_region_p
self.value_opt = FLAGS.value_opt
assert not self.trust_region_p or self.objective in ['pcl', 'trpo']
assert self.objective != 'trpo' or self.trust_region_p
assert self.value_opt is None or self.value_opt == 'None' or \
self.critic_weight == 0.0
self.max_divergence = FLAGS.max_divergence
self.learning_rate = FLAGS.learning_rate
self.clip_norm = FLAGS.clip_norm
self.clip_adv = FLAGS.clip_adv
self.tau = FLAGS.tau
self.tau_decay = FLAGS.tau_decay
self.tau_start = FLAGS.tau_start
self.eps_lambda = FLAGS.eps_lambda
self.update_eps_lambda = FLAGS.update_eps_lambda
self.gamma = FLAGS.gamma
self.rollout = FLAGS.rollout
self.use_target_values = FLAGS.use_target_values
self.fixed_std = FLAGS.fixed_std
self.input_prev_actions = FLAGS.input_prev_actions
self.recurrent = FLAGS.recurrent
assert not self.trust_region_p or not self.recurrent
self.input_time_step = FLAGS.input_time_step
assert not self.input_time_step or (self.cutoff_agent <= self.max_step)
self.use_online_batch = FLAGS.use_online_batch
self.batch_by_steps = FLAGS.batch_by_steps
self.unify_episodes = FLAGS.unify_episodes
if self.unify_episodes:
assert self.batch_size == 1
self.replay_buffer_size = FLAGS.replay_buffer_size
self.replay_buffer_alpha = FLAGS.replay_buffer_alpha
self.replay_buffer_freq = FLAGS.replay_buffer_freq
assert self.replay_buffer_freq in [-1, 0, 1]
self.eviction = FLAGS.eviction
self.prioritize_by = FLAGS.prioritize_by
assert self.prioritize_by in ['rewards', 'step']
self.num_expert_paths = FLAGS.num_expert_paths
self.internal_dim = FLAGS.internal_dim
self.value_hidden_layers = FLAGS.value_hidden_layers
self.tf_seed = FLAGS.tf_seed
self.save_trajectories_dir = FLAGS.save_trajectories_dir
self.save_trajectories_file = (os.path.join(
self.save_trajectories_dir, self.env_str.replace('-', '_'))
if self.save_trajectories_dir else None)
self.load_trajectories_file = FLAGS.load_trajectories_file
self.hparams = dict(
(attr, getattr(self, attr)) for attr in dir(self)
if not attr.startswith('__')
and not isinstance(getattr(self, attr), collections.Callable))