def _prepare_networks(self, hparams, sess):
self.action = tf.placeholder(shape=(1,), dtype=tf.int32)
batch_env = SimulatedBatchEnv(hparams.environment_spec, hparams.num_agents)
self.reward, self.done = batch_env.simulate(self.action)
self.observation = batch_env.observ
self.reset_op = batch_env.reset(tf.constant([0], dtype=tf.int32))
environment_wrappers = hparams.environment_spec.wrappers
wrappers = copy.copy(environment_wrappers) if environment_wrappers else []
to_initialize = [batch_env]
for w in wrappers:
batch_env = w[0](batch_env, **w[1])
to_initialize.append(batch_env)
def initialization_lambda():
for batch_env in to_initialize:
batch_env.initialize(sess)
self.initialize = initialization_lambda
obs_copy = batch_env.observ + 0
actor_critic = get_policy(tf.expand_dims(obs_copy, 0), hparams)
self.policy_probs = actor_critic.policy.probs[0, 0, :]
self.value = actor_critic.value[0, :]
class SimulatedBatchGymEnv(Env):
"""SimulatedBatchEnv in a Gym-like interface, environments are batched."""
def __init__(self, *args, **kwargs):
with tf.Graph().as_default():
self._batch_env = SimulatedBatchEnv(*args, **kwargs)
self._actions_t = tf.placeholder(shape=(self.batch_size, ),
dtype=tf.int32)
self._rewards_t, self._dones_t = self._batch_env.simulate(
self._actions_t)
with tf.control_dependencies([self._rewards_t]):
self._obs_t = self._batch_env.observ
self._indices_t = tf.placeholder(shape=(self.batch_size, ),
dtype=tf.int32)
self._reset_op = self._batch_env.reset(
tf.range(self.batch_size, dtype=tf.int32))
self._sess = tf.Session()
self._sess.run(tf.global_variables_initializer())
self._batch_env.initialize(self._sess)
@property
def batch_size(self):
return self._batch_env.batch_size
@property
def observation_space(self):
return self._batch_env.observ_space
@property
def action_space(self):
return self._batch_env.action_space
def render(self, mode="human"):
raise NotImplementedError()
def reset(self, indices=None):
if indices is None:
indices = np.array(range(self.batch_size))
obs = self._sess.run(self._reset_op,
feed_dict={self._indices_t: indices})
# TODO(pmilos): remove if possible
# obs[:, 0, 0, 0] = 0
# obs[:, 0, 0, 1] = 255
return obs
def step(self, actions):
obs, rewards, dones = self._sess.run(
[self._obs_t, self._rewards_t, self._dones_t],
feed_dict={self._actions_t: actions})
return obs, rewards, dones
def close(self):
self._sess.close()
class SimulatedBatchGymEnv(Env):
"""SimulatedBatchEnv in a Gym-like interface, environments are batched."""
def __init__(self, *args, **kwargs):
with tf.Graph().as_default():
self._batch_env = SimulatedBatchEnv(*args, **kwargs)
self._actions_t = tf.placeholder(shape=(self.batch_size,), dtype=tf.int32)
self._rewards_t, self._dones_t = self._batch_env.simulate(self._actions_t)
self._obs_t = self._batch_env.observ
self._reset_op = self._batch_env.reset(
tf.range(self.batch_size, dtype=tf.int32)
)
self._sess = tf.Session()
self._sess.run(tf.global_variables_initializer())
self._batch_env.initialize(self._sess)
@property
def batch_size(self):
return self._batch_env.batch_size
@property
def observation_space(self):
return self._batch_env.observ_space
@property
def action_space(self):
return self._batch_env.action_space
def render(self, mode="human"):
raise NotImplementedError()
def reset(self, indices=None):
if indices:
raise NotImplementedError()
obs = self._sess.run(self._reset_op)
# TODO(pmilos): remove if possible
# obs[:, 0, 0, 0] = 0
# obs[:, 0, 0, 1] = 255
return obs
def step(self, actions):
obs, rewards, dones = self._sess.run(
[self._obs_t, self._rewards_t, self._dones_t],
feed_dict={self._actions_t: actions})
return obs, rewards, dones
def close(self):
self._sess.close()
def __init__(self, *args, **kwargs):
with tf.Graph().as_default():
self._batch_env = SimulatedBatchEnv(*args, **kwargs)
self._actions_t = tf.placeholder(shape=(self.batch_size, ),
dtype=tf.int32)
self._rewards_t, self._dones_t = self._batch_env.simulate(
self._actions_t)
self._obs_t = self._batch_env.observ
self._reset_op = self._batch_env.reset(
tf.range(self.batch_size, dtype=tf.int32))
self._sess = tf.Session()
self._sess.run(tf.global_variables_initializer())
self._batch_env.initialize(self._sess)
def __init__(self,
environment_spec,
batch_size,
model_dir=None,
sess=None):
self.batch_size = batch_size
with tf.Graph().as_default():
self._batch_env = SimulatedBatchEnv(environment_spec,
self.batch_size)
self.action_space = self._batch_env.action_space
# TODO(kc): check for the stack wrapper and correct number of channels in
# observation_space
self.observation_space = self._batch_env.observ_space
self._sess = sess if sess is not None else tf.Session()
self._to_initialize = [self._batch_env]
environment_wrappers = environment_spec.wrappers
wrappers = copy.copy(
environment_wrappers) if environment_wrappers else []
for w in wrappers:
self._batch_env = w[0](self._batch_env, **w[1])
self._to_initialize.append(self._batch_env)
self._sess.run(tf.global_variables_initializer())
for wrapped_env in self._to_initialize:
wrapped_env.initialize(self._sess)
self._actions_t = tf.placeholder(shape=(batch_size, ),
dtype=tf.int32)
self._rewards_t, self._dones_t = self._batch_env.simulate(
self._actions_t)
self._obs_t = self._batch_env.observ
self._reset_op = self._batch_env.reset(
tf.range(batch_size, dtype=tf.int32))
env_model_loader = tf.train.Saver(
var_list=tf.global_variables(scope="next_frame*")) # pylint:disable=unexpected-keyword-arg
trainer_lib.restore_checkpoint(model_dir,
saver=env_model_loader,
sess=self._sess,
must_restore=True)
def __init__(self, *args, **kwargs):
with tf.Graph().as_default():
self._batch_env = SimulatedBatchEnv(*args, **kwargs)
self._actions_t = tf.placeholder(shape=(self.batch_size,), dtype=tf.int32)
self._rewards_t, self._dones_t = self._batch_env.simulate(self._actions_t)
self._obs_t = self._batch_env.observ
self._reset_op = self._batch_env.reset(
tf.range(self.batch_size, dtype=tf.int32)
)
self._sess = tf.Session()
self._sess.run(tf.global_variables_initializer())
self._batch_env.initialize(self._sess)
def define_collect(hparams, scope):
"""Collect trajectories.
Args:
hparams: HParams.
scope: var scope.
Returns:
Returns memory (observtions, rewards, dones, actions,
pdfs, values_functions)
containing a rollout of environment from nested wrapped structure.
"""
to_initialize = []
with tf.variable_scope(scope, reuse=tf.AUTO_REUSE):
environment_spec = hparams.environment_spec
num_agents = hparams.num_agents
if environment_spec.simulated_env:
batch_env = SimulatedBatchEnv(environment_spec, num_agents)
else:
batch_env = PyFuncBatchEnv(environment_spec.env)
to_initialize.append(batch_env)
environment_wrappers = environment_spec.wrappers
wrappers = copy.copy(environment_wrappers) if environment_wrappers else []
wrappers.append((_MemoryWrapper, {}))
rollout_metadata = None
speculum = None
for w in wrappers:
tf.logging.info("Applying wrapper %s(%s) to env %s."
% (str(w[0]), str(w[1]), str(batch_env)))
batch_env = w[0](batch_env, **w[1])
to_initialize.append(batch_env)
rollout_metadata = _rollout_metadata(batch_env)
speculum = batch_env.speculum
def initialization_lambda(sess):
for batch_env in to_initialize:
batch_env.initialize(sess)
memory = [
tf.get_variable("collect_memory_%d_%s" % (hparams.epoch_length, name),
shape=[hparams.epoch_length] + shape,
dtype=dtype,
initializer=tf.zeros_initializer(),
trainable=False)
for (shape, dtype, name) in rollout_metadata]
cumulative_rewards = tf.get_variable("cumulative_rewards", len(batch_env),
trainable=False)
eval_phase_t = tf.convert_to_tensor(hparams.eval_phase)
should_reset_var = tf.Variable(True, trainable=False)
zeros_tensor = tf.zeros(len(batch_env))
force_beginning_resets = tf.convert_to_tensor(
environment_spec.force_beginning_resets
)
def reset_ops_group():
return tf.group(batch_env.reset(tf.range(len(batch_env))),
tf.assign(cumulative_rewards, zeros_tensor))
reset_op = tf.cond(
tf.logical_or(should_reset_var.read_value(), force_beginning_resets),
reset_ops_group, tf.no_op)
with tf.control_dependencies([reset_op]):
reset_once_op = tf.assign(should_reset_var, False)
with tf.control_dependencies([reset_once_op]):
def step(index, scores_sum, scores_num):
"""Single step."""
index %= hparams.epoch_length # Only needed in eval runs.
# Note - the only way to ensure making a copy of tensor is to run simple
# operation. We are waiting for tf.copy:
# https://github.com/tensorflow/tensorflow/issues/11186
obs_copy = batch_env.observ + 0
def env_step(arg1, arg2, arg3): # pylint: disable=unused-argument
"""Step of the environment."""
actor_critic = get_policy(tf.expand_dims(obs_copy, 0), hparams)
policy = actor_critic.policy
action = hparams.policy_to_actions_lambda(policy)
postprocessed_action = actor_critic.action_postprocessing(action)
reward, done = batch_env.simulate(postprocessed_action[0, ...])
pdf = policy.prob(action)[0]
value_function = actor_critic.value[0]
pdf = tf.reshape(pdf, shape=(num_agents,))
value_function = tf.reshape(value_function, shape=(num_agents,))
done = tf.reshape(done, shape=(num_agents,))
with tf.control_dependencies([reward, done]):
return tf.identity(pdf), tf.identity(value_function), \
tf.identity(done)
# TODO(piotrmilos): while_body is executed at most once,
# thus should be replaced with tf.cond
pdf, value_function, top_level_done = tf.while_loop(
lambda _1, _2, _3: tf.equal(speculum.size(), 0),
env_step,
[
tf.constant(0.0, shape=(num_agents,)),
tf.constant(0.0, shape=(num_agents,)),
tf.constant(False, shape=(num_agents,))
],
parallel_iterations=1,
back_prop=False,
)
with tf.control_dependencies([pdf, value_function]):
obs, reward, done, action = speculum.dequeue()
to_save = [obs, reward, done, action,
pdf, value_function]
save_ops = [tf.scatter_update(memory_slot, index, value)
for memory_slot, value in zip(memory, to_save)]
cumulate_rewards_op = cumulative_rewards.assign_add(reward)
agent_indices_to_reset = tf.where(top_level_done)[:, 0]
with tf.control_dependencies([cumulate_rewards_op]):
# TODO(piotrmilos): possibly we need cumulative_rewards.read_value()
scores_sum_delta = tf.reduce_sum(
tf.gather(cumulative_rewards.read_value(), agent_indices_to_reset))
scores_num_delta = tf.count_nonzero(done, dtype=tf.int32)
with tf.control_dependencies(save_ops + [scores_sum_delta,
scores_num_delta]):
reset_env_op = batch_env.reset(agent_indices_to_reset)
reset_cumulative_rewards_op = tf.scatter_update(
cumulative_rewards, agent_indices_to_reset,
tf.gather(zeros_tensor, agent_indices_to_reset))
with tf.control_dependencies([reset_env_op,
reset_cumulative_rewards_op]):
return [index + 1, scores_sum + scores_sum_delta,
scores_num + scores_num_delta]
def stop_condition(i, _, resets):
return tf.cond(eval_phase_t,
lambda: resets < num_agents,
lambda: i < hparams.epoch_length)
init = [tf.constant(0), tf.constant(0.0), tf.constant(0)]
index, scores_sum, scores_num = tf.while_loop(
stop_condition,
step,
init,
parallel_iterations=1,
back_prop=False)
# We handle force_beginning_resets differently. We assume that all envs are
# reseted at the end of episod (though it happens at the beginning of the
# next one
scores_num = tf.cond(force_beginning_resets,
lambda: scores_num + len(batch_env), lambda: scores_num)
with tf.control_dependencies([scores_sum]):
scores_sum = tf.cond(
force_beginning_resets,
lambda: scores_sum + tf.reduce_sum(cumulative_rewards.read_value()),
lambda: scores_sum)
mean_score = tf.cond(tf.greater(scores_num, 0),
lambda: scores_sum / tf.cast(scores_num, tf.float32),
lambda: 0.)
printing = tf.Print(0, [mean_score, scores_sum, scores_num], "mean_score: ")
with tf.control_dependencies([index, printing]):
memory = [mem.read_value() for mem in memory]
# When generating real data together with PPO training we must use single
# agent. For PPO to work we reshape the history, as if it was generated
# by real_ppo_effective_num_agents.
if (getattr(hparams, "effective_num_agents", None) and
not hparams.eval_phase):
new_memory = []
effective_num_agents = hparams.effective_num_agents
assert hparams.epoch_length % effective_num_agents == 0, (
"The rollout of hparams.epoch_length will be distributed amongst"
"effective_num_agents of agents")
new_epoch_length = int(hparams.epoch_length / effective_num_agents)
for mem, info in zip(memory, rollout_metadata):
shape, _, name = info
new_shape = [effective_num_agents, new_epoch_length] + shape[1:]
perm = list(range(len(shape)+1))
perm[0] = 1
perm[1] = 0
mem = tf.transpose(mem, perm=perm)
mem = tf.reshape(mem, shape=new_shape)
mem = tf.transpose(mem, perm=perm,
name="collect_memory_%d_%s"
% (new_epoch_length, name))
new_memory.append(mem)
memory = new_memory
mean_score_summary = tf.cond(
tf.greater(scores_num, 0),
lambda: tf.summary.scalar("mean_score_this_iter", mean_score),
str)
summaries = tf.summary.merge(
[mean_score_summary,
tf.summary.scalar("episodes_finished_this_iter", scores_num)])
return memory, summaries, initialization_lambda
class SimulatedBatchGymEnv(Env):
"""SimulatedBatchEnv in a Gym-like interface, environments are batched."""
def __init__(self,
environment_spec,
batch_size,
model_dir=None,
sess=None):
self.batch_size = batch_size
with tf.Graph().as_default():
self._batch_env = SimulatedBatchEnv(environment_spec,
self.batch_size)
self.action_space = self._batch_env.action_space
# TODO(kc): check for the stack wrapper and correct number of channels in
# observation_space
self.observation_space = self._batch_env.observ_space
self._sess = sess if sess is not None else tf.Session()
self._to_initialize = [self._batch_env]
environment_wrappers = environment_spec.wrappers
wrappers = copy.copy(
environment_wrappers) if environment_wrappers else []
for w in wrappers:
self._batch_env = w[0](self._batch_env, **w[1])
self._to_initialize.append(self._batch_env)
self._sess.run(tf.global_variables_initializer())
for wrapped_env in self._to_initialize:
wrapped_env.initialize(self._sess)
self._actions_t = tf.placeholder(shape=(batch_size, ),
dtype=tf.int32)
self._rewards_t, self._dones_t = self._batch_env.simulate(
self._actions_t)
self._obs_t = self._batch_env.observ
self._reset_op = self._batch_env.reset(
tf.range(batch_size, dtype=tf.int32))
env_model_loader = tf.train.Saver(
var_list=tf.global_variables(scope="next_frame*")) # pylint:disable=unexpected-keyword-arg
trainer_lib.restore_checkpoint(model_dir,
saver=env_model_loader,
sess=self._sess,
must_restore=True)
def render(self, mode="human"):
raise NotImplementedError()
def reset(self, indices=None):
if indices:
raise NotImplementedError()
obs = self._sess.run(self._reset_op)
# TODO(pmilos): remove if possible
# obs[:, 0, 0, 0] = 0
# obs[:, 0, 0, 1] = 255
return obs
def step(self, actions):
obs, rewards, dones = self._sess.run(
[self._obs_t, self._rewards_t, self._dones_t],
feed_dict={self._actions_t: actions})
return obs, rewards, dones