def rollout_episode(
domain: Domain,
solver: Optional[Union[Solver, Policies]] = None,
from_memory: Optional[D.T_memory[D.T_state]] = None,
from_action: Optional[D.T_agent[D.T_concurrency[D.T_event]]] = None,
num_episodes: int = 1,
max_steps: Optional[int] = None,
render: bool = True,
max_framerate: Optional[float] = None,
verbose: bool = True,
action_formatter: Optional[Callable[[D.T_event], str]] = None,
outcome_formatter: Optional[Callable[[EnvironmentOutcome], str]] = None,
save_result_directory: str = None
) -> Tuple[List[D.T_observation], List[D.T_event], List[D.T_value]]:
"""This method will run one or more episodes in a domain according to the policy of a solver.
# Parameters
domain: The domain in which the episode(s) will be run.
solver: The solver whose policy will select actions to take (if None, a random policy is used).
from_memory: The memory or state to consider as rollout starting point (if None, the domain is reset first).
from_action: The last applied action when from_memory is used (if necessary for initial observation computation).
num_episodes: The number of episodes to run.
max_steps: The maximum number of steps for each episode (if None, no limit is set).
render: Whether to render the episode(s) during rollout if the domain is renderable.
max_framerate: The maximum number of steps/renders per second (if None, steps/renders are never slowed down).
verbose: Whether to print information to the console during rollout.
action_formatter: The function transforming actions in the string to print (if None, no print).
outcome_formatter: The function transforming EnvironmentOutcome objects in the string to print (if None, no print).
save_result: Directory in which state visited, actions applied and Transition Value are saved to json.
"""
if verbose:
logger.setLevel(logging.DEBUG)
logger.debug(
'Logger is in verbose mode: all debug messages will be there for you to enjoy （〜^∇^ )〜'
)
if solver is None:
# Create solver-like random walker that works for any domain
class RandomWalk(Policies):
T_domain = Domain
T_agent = Domain.T_agent
T_event = Domain.T_event
def __init__(self):
class CastDomain: # trick to autocast domain's get_applicable_actions() without mutating domain
T_agent = domain.T_agent
T_event = domain.T_event
@autocastable
def get_applicable_actions(
self) -> D.T_agent[Space[D.T_event]]:
return domain.get_applicable_actions()
self._domain = CastDomain()
autocast_all(self._domain, self._domain, self)
@autocastable
def reset(self) -> None:
pass
@autocastable
def sample_action(
self, observation: D.T_agent[D.T_observation]
) -> D.T_agent[D.T_concurrency[D.T_event]]:
return {
agent: [space.sample()]
for agent, space in
self._domain.get_applicable_actions().items()
}
@autocastable
def is_policy_defined_for(
self, observation: D.T_agent[D.T_observation]) -> bool:
return True
solver = RandomWalk()
autocast_all(solver, solver.T_domain, domain)
has_render = isinstance(domain, Renderable)
has_goal = isinstance(domain, Goals)
has_memory = not isinstance(domain, Markovian)
for i_episode in range(num_episodes):
# Initialize episode
solver.reset()
if from_memory is None:
#observation = domain.reset()
pass
else:
domain.set_memory(from_memory)
last_state = from_memory[-1] if has_memory else from_memory
observation = domain.get_observation_distribution(
last_state, from_action).sample()
if verbose:
logger.debug(
f'Episode {i_episode + 1} started with following observation:')
logger.debug(observation)
# Run episode
step = 1
observations = []
actions = []
values = []
# save the initial observation
observations.append(observation)
while max_steps is None or step <= max_steps:
old_time = time.perf_counter()
if render and has_render:
domain.render()
action = solver.sample_action(observation)
if action_formatter is not None:
logger.debug('Action: {}'.format(action_formatter(action)))
domain.set_memory(observations[-1])
outcome = domain.step(action)
observation = outcome.observation
observations.append(observation)
actions.append(action)
values.append(outcome.value)
if outcome_formatter is not None:
logger.debug('Result: {}'.format(outcome_formatter(outcome)))
if outcome.termination:
logger.debug(
f'Episode {i_episode + 1} terminated after {step + 1} steps.'
)
break
if max_framerate is not None:
wait = 1 / max_framerate - (time.perf_counter() - old_time)
if wait > 0:
time.sleep(wait)
step += 1
if render and has_render:
domain.render()
if has_goal and verbose:
logger.info(
f'The goal was{"" if domain.is_goal(observation) else " not"} reached '
f'in episode {i_episode + 1}.')
return observations, actions, values
def rollout(domain: Domain,
solver: Optional[Solver] = None,
from_memory: Optional[D.T_memory[D.T_state]] = None,
from_action: Optional[D.T_agent[D.T_concurrency[
D.T_event]]] = None,
num_episodes: int = 1,
max_steps: Optional[int] = None,
render: bool = True,
max_framerate: Optional[float] = None,
verbose: bool = True,
action_formatter: Optional[Callable[[D.T_event],
str]] = lambda a: str(a),
outcome_formatter: Optional[Callable[[EnvironmentOutcome],
str]] = lambda o: str(o),
save_result_directory: str = None) -> str:
"""This method will run one or more episodes in a domain according to the policy of a solver.
# Parameters
domain: The domain in which the episode(s) will be run.
solver: The solver whose policy will select actions to take (if None, a random policy is used).
from_memory: The memory or state to consider as rollout starting point (if None, the domain is reset first).
from_action: The last applied action when from_memory is used (if necessary for initial observation computation).
num_episodes: The number of episodes to run.
max_steps: The maximum number of steps for each episode (if None, no limit is set).
render: Whether to render the episode(s) during rollout if the domain is renderable.
max_framerate: The maximum number of steps/renders per second (if None, steps/renders are never slowed down).
verbose: Whether to print information to the console during rollout.
action_formatter: The function transforming actions in the string to print (if None, no print).
outcome_formatter: The function transforming EnvironmentOutcome objects in the string to print (if None, no print).
save_result: Directory in which state visited, actions applied and Transition Value are saved to json.
"""
if verbose:
logger.setLevel(logging.DEBUG)
logger.debug(
'Logger is in verbose mode: all debug messages will be there for you to enjoy （〜^∇^ )〜'
)
if solver is None:
# Create solver-like random walker that works for any domain
class RandomWalk(Policies):
T_domain = Domain
T_agent = Domain.T_agent
T_event = Domain.T_event
def __init__(self):
class CastDomain: # trick to autocast domain's get_applicable_actions() without mutating domain
T_agent = domain.T_agent
T_event = domain.T_event
@autocastable
def get_applicable_actions(
self) -> D.T_agent[Space[D.T_event]]:
return domain.get_applicable_actions()
self._domain = CastDomain()
autocast_all(self._domain, self._domain, self)
@autocastable
def reset(self) -> None:
pass
@autocastable
def sample_action(
self, observation: D.T_agent[D.T_observation]
) -> D.T_agent[D.T_concurrency[D.T_event]]:
return {
agent: [space.sample()]
for agent, space in
self._domain.get_applicable_actions().items()
}
@autocastable
def is_policy_defined_for(
self, observation: D.T_agent[D.T_observation]) -> bool:
return True
solver = RandomWalk()
autocast_all(solver, solver.T_domain, domain)
has_render = isinstance(domain, Renderable)
has_goal = isinstance(domain, Goals)
has_memory = not isinstance(domain, Markovian)
for i_episode in range(num_episodes):
# Initialize episode
solver.reset()
if from_memory is None:
observation = domain.reset()
else:
domain.set_memory(from_memory)
last_state = from_memory[-1] if has_memory else from_memory
observation = domain.get_observation_distribution(
last_state, from_action).sample()
logger.debug(
f'Episode {i_episode + 1} started with following observation:')
logger.debug(observation)
# Run episode
step = 1
if save_result_directory is not None:
observations = dict()
transitions = dict()
actions = dict()
# save the initial observation
observations[0] = observation
while max_steps is None or step <= max_steps:
old_time = time.perf_counter()
if render and has_render:
domain.render()
# assert solver.is_policy_defined_for(observation)
if save_result_directory is not None:
previous_observation = copy.deepcopy(observation)
action = solver.sample_action(observation)
if action_formatter is not None:
logger.debug('Action: {}'.format(action_formatter(action)))
outcome = domain.step(action)
observation = outcome.observation
if save_result_directory is not None:
if isinstance(domain, FullyObservable):
observations[step] = observation
actions[step] = action
transitions[step] = {
"s": hash(previous_observation),
"a": hash(action),
"cost": outcome.value.cost,
"s'": hash(observation)
}
if outcome_formatter is not None:
logger.debug('Result: {}'.format(outcome_formatter(outcome)))
if outcome.termination:
logger.debug(
f'Episode {i_episode + 1} terminated after {step + 1} steps.'
)
break
if max_framerate is not None:
wait = 1 / max_framerate - (time.perf_counter() - old_time)
if wait > 0:
time.sleep(wait)
step += 1
if render and has_render:
domain.render()
if has_goal:
logger.info(
f'The goal was{"" if domain.is_goal(observation) else " not"} reached '
f'in episode {i_episode + 1}.')
if save_result_directory is not None:
if not os.path.exists(save_result_directory):
os.mkdir(save_result_directory)
elif not os.path.isdir(save_result_directory):
raise FileExistsError
now = datetime.datetime.now()
str_timestamp = now.strftime("%Y%m%dT%H%M%S")
directory = os.path.join(save_result_directory, str_timestamp)
os.mkdir(directory)
try:
with open(os.path.join(directory, 'actions.json'), 'w') as f:
json.dump(actions, f, indent=2)
except TypeError:
logger.error("Action is not serializable")
try:
with open(os.path.join(directory, 'transitions.json'),
'w') as f:
json.dump(transitions, f, indent=2)
except TypeError:
logger.error("Transition is not serializable")
try:
with open(os.path.join(directory, 'observations.json'),
'w') as f:
json.dump(observations, f, indent=2)
except TypeError:
logger.error("Observation is not serializable")
return directory