"""Default Worker class."""
from collections import defaultdict
import gym
import numpy as np
from garage import TrajectoryBatch
from garage.experiment import deterministic
from garage.sampler.env_update import EnvUpdate
from garage.sampler.worker import Worker
[docs]class DefaultWorker(Worker):
"""Initialize a worker.
Args:
seed(int): The seed to use to intialize random number generators.
max_path_length(int or float): The maximum length paths which will
be sampled. Can be (floating point) infinity.
worker_number(int): The number of the worker where this update is
occurring. This argument is used to set a different seed for each
worker.
Attributes:
agent(Policy or None): The worker's agent.
env(gym.Env or None): The worker's environment.
"""
def __init__(
self,
*, # Require passing by keyword, since everything's an int.
seed,
max_path_length,
worker_number):
super().__init__(seed=seed,
max_path_length=max_path_length,
worker_number=worker_number)
self.agent = None
self.env = None
self._observations = []
self._last_observations = []
self._actions = []
self._rewards = []
self._terminals = []
self._lengths = []
self._agent_infos = defaultdict(list)
self._env_infos = defaultdict(list)
self._prev_obs = None
self._path_length = 0
self.worker_init()
[docs] def worker_init(self):
"""Initialize a worker."""
if self._seed is not None:
deterministic.set_seed(self._seed + self._worker_number)
[docs] def update_agent(self, agent_update):
"""Update an agent, assuming it implements garage.Policy.
Args:
agent_update (np.ndarray or dict or garage.Policy): If a
tuple, dict, or np.ndarray, these should be parameters to
agent, which should have been generated by calling
`policy.get_param_values`. Alternatively, a policy itself. Note
that other implementations of `Worker` may take different types
for this parameter.
"""
if isinstance(agent_update, (dict, tuple, np.ndarray)):
self.agent.set_param_values(agent_update)
elif agent_update is not None:
self.agent = agent_update
[docs] def update_env(self, env_update):
"""Use any non-None env_update as a new environment.
A simple env update function. If env_update is not None, it should be
the complete new environment.
This allows changing environments by passing the new environment as
`env_update` into `obtain_samples`.
Args:
env_update(gym.Env or EnvUpdate or None): The environment to
replace the existing env with. Note that other implementations
of `Worker` may take different types for this parameter.
Raises:
TypeError: If env_update is not one of the documented types.
"""
if env_update is not None:
if isinstance(env_update, EnvUpdate):
self.env = env_update(self.env)
elif isinstance(env_update, gym.Env):
if self.env is not None:
self.env.close()
self.env = env_update
else:
raise TypeError('Uknown environment update type.')
[docs] def start_rollout(self):
"""Begin a new rollout."""
self._path_length = 0
self._prev_obs = self.env.reset()
self.agent.reset()
[docs] def step_rollout(self):
"""Take a single time-step in the current rollout.
Returns:
bool: True iff the path is done, either due to the environment
indicating termination of due to reaching `max_path_length`.
"""
if self._path_length < self._max_path_length:
a, agent_info = self.agent.get_action(self._prev_obs)
next_o, r, d, env_info = self.env.step(a)
self._observations.append(self._prev_obs)
self._rewards.append(r)
self._actions.append(a)
for k, v in agent_info.items():
self._agent_infos[k].append(v)
for k, v in env_info.items():
self._env_infos[k].append(v)
self._path_length += 1
self._terminals.append(d)
if not d:
self._prev_obs = next_o
return False
self._lengths.append(self._path_length)
self._last_observations.append(self._prev_obs)
return True
[docs] def collect_rollout(self):
"""Collect the current rollout, clearing the internal buffer.
Returns:
garage.TrajectoryBatch: A batch of the trajectories completed since
the last call to collect_rollout().
"""
observations = self._observations
self._observations = []
last_observations = self._last_observations
self._last_observations = []
actions = self._actions
self._actions = []
rewards = self._rewards
self._rewards = []
terminals = self._terminals
self._terminals = []
env_infos = self._env_infos
self._env_infos = defaultdict(list)
agent_infos = self._agent_infos
self._agent_infos = defaultdict(list)
for k, v in agent_infos.items():
agent_infos[k] = np.asarray(v)
for k, v in env_infos.items():
env_infos[k] = np.asarray(v)
lengths = self._lengths
self._lengths = []
return TrajectoryBatch(self.env.spec, np.asarray(observations),
np.asarray(last_observations),
np.asarray(actions), np.asarray(rewards),
np.asarray(terminals), dict(env_infos),
dict(agent_infos), np.asarray(lengths,
dtype='i'))
[docs] def rollout(self):
"""Sample a single rollout of the agent in the environment.
Returns:
garage.TrajectoryBatch: The collected trajectory.
"""
self.start_rollout()
while not self.step_rollout():
pass
return self.collect_rollout()
[docs] def shutdown(self):
"""Close the worker's environment."""
self.env.close()