def evaluate(params):
# file params
experiment_path = os.path.join(params.output_data_dir)
logger.experiment_path = os.path.join(experiment_path, 'evaluation')
params.checkpoint_restore_dir = os.path.join(params.input_data_dir, 'checkpoint')
checkpoint_file = os.path.join(params.checkpoint_restore_dir, 'checkpoint')
inplace_change(checkpoint_file, "/opt/ml/output/data/checkpoint", ".")
# Note that due to a tensorflow issue (https://github.com/tensorflow/tensorflow/issues/9146) we need to replace
# the absolute path for the evaluation-from-a-checkpointed-model to work
vis_params = VisualizationParameters()
vis_params.dump_gifs = True
task_params = TaskParameters(evaluate_only=True, experiment_path=logger.experiment_path)
task_params.__dict__ = add_items_to_dict(task_params.__dict__, params.__dict__)
graph_manager = BasicRLGraphManager(
agent_params=ClippedPPOAgentParameters(),
env_params=GymVectorEnvironment(level='TSP_env:TSPEasyEnv'),
schedule_params=ScheduleParameters(),
vis_params=vis_params
)
graph_manager = graph_manager.create_graph(task_parameters=task_params)
graph_manager.evaluate(EnvironmentSteps(5))
def evaluate(params):
# file params
experiment_path = os.path.join(params.output_data_dir)
logger.experiment_path = os.path.join(experiment_path, 'evaluation')
params.checkpoint_restore_dir = os.path.join(params.input_data_dir,
'checkpoint')
checkpoint_file = os.path.join(params.checkpoint_restore_dir, 'checkpoint')
inplace_change(checkpoint_file, "/opt/ml/output/data/checkpoint", ".")
# Note that due to a tensorflow issue (https://github.com/tensorflow/tensorflow/issues/9146) we need to replace
# the absolute path for the evaluation-from-a-checkpointed-model to work
vis_params = VisualizationParameters()
vis_params.dump_gifs = True
task_params = TaskParameters(evaluate_only=True,
experiment_path=logger.experiment_path)
task_params.__dict__ = add_items_to_dict(task_params.__dict__,
params.__dict__)
graph_manager = BasicRLGraphManager(
agent_params=ClippedPPOAgentParameters(),
env_params=GymVectorEnvironment(level='TSP_env:TSPEasyEnv'),
schedule_params=ScheduleParameters(),
vis_params=vis_params)
graph_manager = graph_manager.create_graph(task_parameters=task_params)
graph_manager.evaluate(EnvironmentSteps(5))
level = 'gym_dynamic_multi_armed_bandit.envs:BasicEnv2'
env_params = GymVectorEnvironment(level)
########################
# Create Graph Manager #
########################
graph_manager = BasicRLGraphManager(agent_params=agent_params,
env_params=env_params,
schedule_params=schedule)
#######################
# add task parameters #
#######################
log_path = './experiments_v2/log' # training logs are saved
checkpoint_sec = 60 # checkpoints are used to restore the model
if not os.path.exists(log_path):
os.makedirs(log_path)
task_parameters = TaskParameters(evaluate_only=False,
experiment_path=log_path,
checkpoint_save_secs=checkpoint_sec)
graph_manager.create_graph(task_parameters)
##################
# start training #
##################
graph_manager.improve()
