def _create_graph(
self, task_parameters: TaskParameters
) -> Tuple[List[MultiAgentLevelManager], List[Environment]]:
# environment loading
self.env_params.seed = task_parameters.seed
self.env_params.experiment_path = task_parameters.experiment_path
env = short_dynamic_import(self.env_params.path)(
**self.env_params.__dict__,
visualization_parameters=self.visualization_parameters)
# agent loading
agents = OrderedDict()
for agent_params in self.agents_params:
agent_params.task_parameters = copy.copy(task_parameters)
agent = short_dynamic_import(agent_params.path)(agent_params)
agents[agent_params.name] = agent
screen.log_title("Created agent: {}".format(agent_params.name))
if hasattr(self, 'memory_backend_params') and \
self.memory_backend_params.run_type == str(RunType.ROLLOUT_WORKER):
agent.memory.memory_backend = deepracer_memory.DeepRacerRolloutBackEnd(
self.memory_backend_params,
agent_params.algorithm.num_consecutive_playing_steps,
agent_params.name)
# set level manager
level_manager = MultiAgentLevelManager(
agents=agents,
environment=env,
name="main_level",
done_condition=self.done_condition)
return [level_manager], [env]
def rollout_worker(graph_manager, checkpoint_dir, data_store, num_workers,
memory_backend_params):
"""
wait for first checkpoint then perform rollouts using the model
"""
wait_for_checkpoint(checkpoint_dir, data_store)
task_parameters = TaskParameters()
task_parameters.__dict__['checkpoint_restore_dir'] = checkpoint_dir
graph_manager.create_graph(task_parameters)
graph_manager.reset_internal_state()
for level in graph_manager.level_managers:
for agent in level.agents.values():
agent.memory.memory_backend = deepracer_memory.DeepRacerRolloutBackEnd(
memory_backend_params, graph_manager.agent_params.algorithm.
num_consecutive_playing_steps)
try:
with graph_manager.phase_context(RunPhase.TRAIN):
last_checkpoint = 0
act_steps = math.ceil(
(graph_manager.agent_params.algorithm.
num_consecutive_playing_steps.num_steps) / num_workers)
for i in range(
int(graph_manager.improve_steps.num_steps / act_steps)):
if should_stop(checkpoint_dir):
break
try:
# This will only work for DeepRacerRacetrackEnv enviroments
graph_manager.top_level_manager.environment.env.env.set_allow_servo_step_signals(
True)
except Exception as ex:
utils.json_format_logger(
"Method not defined in enviroment class: {}".format(
ex),
**utils.build_system_error_dict(
utils.SIMAPP_SIMULATION_WORKER_EXCEPTION,
utils.SIMAPP_EVENT_ERROR_CODE_500))
if type(graph_manager.agent_params.algorithm.
num_consecutive_playing_steps) == EnvironmentSteps:
graph_manager.act(
EnvironmentSteps(num_steps=act_steps),
wait_for_full_episodes=graph_manager.agent_params.
algorithm.act_for_full_episodes)
elif type(
graph_manager.agent_params.algorithm.
num_consecutive_playing_steps) == EnvironmentEpisodes:
graph_manager.act(EnvironmentEpisodes(num_steps=act_steps))
try:
# This will only work for DeepRacerRacetrackEnv enviroments
graph_manager.top_level_manager.environment.env.env.set_allow_servo_step_signals(
False)
graph_manager.top_level_manager.environment.env.env.stop_car(
)
except Exception as ex:
utils.json_format_logger(
"Method not defined in enviroment class: {}".format(
ex),
**utils.build_system_error_dict(
utils.SIMAPP_SIMULATION_WORKER_EXCEPTION,
utils.SIMAPP_EVENT_ERROR_CODE_500))
if graph_manager.agent_params.algorithm.distributed_coach_synchronization_type == DistributedCoachSynchronizationType.SYNC:
data_store.load_from_store(
expected_checkpoint_number=last_checkpoint + 1)
last_checkpoint = get_latest_checkpoint(checkpoint_dir)
graph_manager.restore_checkpoint()
if graph_manager.agent_params.algorithm.distributed_coach_synchronization_type == DistributedCoachSynchronizationType.ASYNC:
new_checkpoint = get_latest_checkpoint(checkpoint_dir)
if new_checkpoint > last_checkpoint:
graph_manager.restore_checkpoint()
last_checkpoint = new_checkpoint
except Exception as ex:
utils.json_format_logger(
"An error occured during simulation: {}".format(ex),
**utils.build_system_error_dict(
utils.SIMAPP_SIMULATION_WORKER_EXCEPTION,
utils.SIMAPP_EVENT_ERROR_CODE_500))
