def main():
parser = argparse.ArgumentParser(description="Hyperparameter search for implemented RL models")
parser.add_argument('--optimizer', default='hyperband', choices=['hyperband', 'hyperopt'], type=str,
help='The hyperparameter optimizer to choose from')
parser.add_argument('--algo', default='ppo2', choices=list(registered_rl.keys()), help='OpenAI baseline to use',
type=str)
parser.add_argument('--env', type=str, help='environment ID', default='KukaButtonGymEnv-v0',
choices=list(registered_env.keys()))
parser.add_argument('--seed', type=int, default=0, help='random seed (default: 0)')
parser.add_argument('--srl-model', type=str, default='raw_pixels', choices=list(registered_srl.keys()),
help='SRL model to use')
parser.add_argument('--num-timesteps', type=int, default=1e6, help='number of timesteps the baseline should run')
parser.add_argument('-v', '--verbose', action='store_true', default=False, help='Display baseline STDOUT')
parser.add_argument('--max-eval', type=int, default=100, help='Number of evalutation to try for hyperopt')
args, train_args = parser.parse_known_args()
args.log_dir = "logs/_{}_search/".format(args.optimizer)
train_args.extend(['--srl-model', args.srl_model, '--seed', str(args.seed), '--algo', args.algo, '--env', args.env,
'--log-dir', args.log_dir, '--no-vis'])
# verify the algorithm has defined it, and that it returnes an expected value
try:
opt_param = registered_rl[args.algo][0].getOptParam()
assert opt_param is not None
except AttributeError or AssertionError:
raise AssertionError("Error: {} algo does not support hyperparameter search.".format(args.algo))
if args.optimizer == "hyperband":
opt = Hyperband(opt_param, makeRlTrainingFunction(args, train_args), seed=args.seed,
max_iter=args.num_timesteps // ITERATION_SCALE)
elif args.optimizer == "hyperopt":
opt = Hyperopt(opt_param, makeRlTrainingFunction(args, train_args), seed=args.seed, num_eval=args.max_eval)
else:
raise ValueError("Error: optimizer {} was defined but not implemented, Halting.".format(args.optimizer))
t_start = time.time()
opt.run()
all_params, loss = zip(*opt.history)
idx = np.argmin(loss)
opt_params, nb_iter = all_params[idx]
reward = loss[idx]
print('\ntime to run : {}s'.format(int(time.time() - t_start)))
print('Total nb. evaluations : {}'.format(len(all_params)))
if nb_iter is not None:
print('Best nb. of iterations : {}'.format(int(nb_iter)))
print('Best params : ')
pprint.pprint(opt_params)
print('Best reward : {:.3f}'.format(-reward))
param_dict, timesteps = zip(*all_params)
output = pd.DataFrame(list(param_dict))
# make sure we returned a timestep value to log, otherwise ignore
if not any([el is None for el in timesteps]):
output["timesteps"] = np.array(np.maximum(MIN_ITERATION, np.array(timesteps) * ITERATION_SCALE).astype(int))
output["reward"] = -np.array(loss)
output.to_csv("logs/{}_{}_{}_{}_seed{}_numtimestep{}.csv"
.format(args.optimizer, args.algo, args.env, args.srl_model, args.seed, args.num_timesteps))
def main():
# Global variables for callback
global ENV_NAME, ALGO, ALGO_NAME, LOG_INTERVAL, VISDOM_PORT, viz
global SAVE_INTERVAL, EPISODE_WINDOW, MIN_EPISODES_BEFORE_SAVE
parser = argparse.ArgumentParser(
description="Train script for RL algorithms")
parser.add_argument('--algo',
default='ppo2',
choices=list(registered_rl.keys()),
help='RL algo to use',
type=str)
parser.add_argument('--env',
type=str,
help='environment ID',
default='KukaButtonGymEnv-v0',
choices=list(registered_env.keys()))
parser.add_argument('--seed',
type=int,
default=0,
help='random seed (default: 0)')
parser.add_argument(
'--episode-window',
type=int,
default=40,
help='Episode window for moving average plot (default: 40)')
parser.add_argument(
'--log-dir',
default='/tmp/gym/',
type=str,
help='directory to save agent logs and model (default: /tmp/gym)')
parser.add_argument('--num-timesteps', type=int, default=int(1e6))
parser.add_argument('--srl-model',
type=str,
default='raw_pixels',
choices=list(registered_srl.keys()),
help='SRL model to use')
parser.add_argument('--num-stack',
type=int,
default=1,
help='number of frames to stack (default: 1)')
parser.add_argument(
'--action-repeat',
type=int,
default=1,
help='number of times an action will be repeated (default: 1)')
parser.add_argument('--port',
type=int,
default=8097,
help='visdom server port (default: 8097)')
parser.add_argument('--no-vis',
action='store_true',
default=False,
help='disables visdom visualization')
parser.add_argument(
'--shape-reward',
action='store_true',
default=False,
help='Shape the reward (reward = - distance) instead of a sparse reward'
)
parser.add_argument('-c',
'--continuous-actions',
action='store_true',
default=False)
parser.add_argument(
'-joints',
'--action-joints',
action='store_true',
default=False,
help=
'set actions to the joints of the arm directly, instead of inverse kinematics'
)
parser.add_argument('-r',
'--random-target',
action='store_true',
default=False,
help='Set the button to a random position')
parser.add_argument(
'--srl-config-file',
type=str,
default="config/srl_models.yaml",
help='Set the location of the SRL model path configuration.')
parser.add_argument('--hyperparam', type=str, nargs='+', default=[])
parser.add_argument('--min-episodes-save',
type=int,
default=100,
help="Min number of episodes before saving best model")
parser.add_argument(
'--latest',
action='store_true',
default=False,
help=
'load the latest learned model (location:srl_zoo/logs/DatasetName/)')
parser.add_argument(
'--load-rl-model-path',
type=str,
default=None,
help="load the trained RL model, should be with the same algorithm type"
)
parser.add_argument(
'-sc',
'--simple-continual',
action='store_true',
default=False,
help=
'Simple red square target for task 1 of continual learning scenario. '
+ 'The task is: robot should reach the target.')
parser.add_argument(
'-cc',
'--circular-continual',
action='store_true',
default=False,
help='Blue square target for task 2 of continual learning scenario. ' +
'The task is: robot should turn in circle around the target.')
parser.add_argument(
'-sqc',
'--square-continual',
action='store_true',
default=False,
help='Green square target for task 3 of continual learning scenario. '
+ 'The task is: robot should turn in square around the target.')
parser.add_argument(
'-ec',
'--eight-continual',
action='store_true',
default=False,
help='Green square target for task 4 of continual learning scenario. '
+
'The task is: robot should do the eigth with the target as center of the shape.'
)
parser.add_argument('--teacher-data-folder',
type=str,
default="",
help='Dataset folder of the teacher(s) policy(ies)',
required=False)
parser.add_argument(
'--epochs-distillation',
type=int,
default=30,
metavar='N',
help='number of epochs to train for distillation(default: 30)')
parser.add_argument(
'--distillation-training-set-size',
type=int,
default=-1,
help='Limit size (number of samples) of the training set (default: -1)'
)
parser.add_argument(
'--perform-cross-evaluation-cc',
action='store_true',
default=False,
help='A cross evaluation from the latest stored model to all tasks')
parser.add_argument(
'--eval-episode-window',
type=int,
default=400,
metavar='N',
help=
'Episode window for saving each policy checkpoint for future distillation(default: 100)'
)
parser.add_argument(
'--new-lr',
type=float,
default=1.e-4,
help="New learning rate ratio to train a pretrained agent")
parser.add_argument('--img-shape',
type=str,
default="(3,64,64)",
help="Image shape of environment.")
parser.add_argument(
"--gpu-num",
help="Choose the number of GPU (CUDA_VISIBLE_DEVICES).",
type=str,
default="1",
choices=["0", "1", "2", "3", "5", "6", "7", "8"])
parser.add_argument("--srl-model-path",
help="SRL model weights path",
type=str,
default=None)
parser.add_argument(
"--relative-pos",
action='store_true',
default=False,
help="For 'ground_truth': use relative position or not.")
# Ignore unknown args for now
args, unknown = parser.parse_known_args()
# os.environ["CUDA_VISIBLE_DEVICES"] = args.gpu_num
env_kwargs = {}
if args.img_shape is None:
img_shape = None #(3,224,224)
else:
img_shape = tuple(map(int, args.img_shape[1:-1].split(",")))
env_kwargs['img_shape'] = img_shape
# LOAD SRL models list
assert os.path.exists(args.srl_config_file), \
"Error: cannot load \"--srl-config-file {}\", file not found!".format(args.srl_config_file)
with open(args.srl_config_file, 'rb') as f:
all_models = yaml.load(f)
# Sanity check
assert args.episode_window >= 1, "Error: --episode_window cannot be less than 1"
assert args.num_timesteps >= 1, "Error: --num-timesteps cannot be less than 1"
assert args.num_stack >= 1, "Error: --num-stack cannot be less than 1"
assert args.action_repeat >= 1, "Error: --action-repeat cannot be less than 1"
assert 0 <= args.port < 65535, "Error: invalid visdom port number {}, ".format(args.port) + \
"port number must be an unsigned 16bit number [0,65535]."
assert registered_srl[args.srl_model][0] == SRLType.ENVIRONMENT or args.env in all_models, \
"Error: the environment {} has no srl_model defined in 'srl_models.yaml'. Cannot continue.".format(args.env)
# check that all the SRL_model can be run on the environment
if registered_srl[args.srl_model][1] is not None:
found = False
for compatible_class in registered_srl[args.srl_model][1]:
if issubclass(compatible_class, registered_env[args.env][0]):
found = True
break
assert found, "Error: srl_model {}, is not compatible with the {} environment.".format(
args.srl_model, args.env)
assert not(sum([args.simple_continual, args.circular_continual, args.square_continual, args.eight_continual]) \
> 1 and args.env == "OmnirobotEnv-v0"), \
"For continual SRL and RL, please provide only one scenario at the time and use OmnirobotEnv-v0 environment !"
assert not(args.algo == "distillation" and (args.teacher_data_folder == '' or args.continuous_actions is True)), \
"For performing policy distillation, make sure use specify a valid teacher dataset and discrete actions !"
ENV_NAME = args.env
ALGO_NAME = args.algo
VISDOM_PORT = args.port
EPISODE_WINDOW = args.episode_window
MIN_EPISODES_BEFORE_SAVE = args.min_episodes_save
CROSS_EVAL = args.perform_cross_evaluation_cc
EPISODE_WINDOW_DISTILLATION_WIN = args.eval_episode_window
NEW_LR = args.new_lr
print("EPISODE_WINDOW_DISTILLATION_WIN: ", EPISODE_WINDOW_DISTILLATION_WIN)
if args.no_vis:
viz = False
algo_class, algo_type, action_type = registered_rl[args.algo]
algo = algo_class()
ALGO = algo
# if callback frequency needs to be changed
LOG_INTERVAL = algo.LOG_INTERVAL
SAVE_INTERVAL = algo.SAVE_INTERVAL
if not args.continuous_actions and ActionType.DISCRETE not in action_type:
raise ValueError(
args.algo +
" does not support discrete actions, please use the '--continuous-actions' "
+ "(or '-c') flag.")
if args.continuous_actions and ActionType.CONTINUOUS not in action_type:
raise ValueError(
args.algo +
" does not support continuous actions, please remove the '--continuous-actions' "
+ "(or '-c') flag.")
env_kwargs["is_discrete"] = not args.continuous_actions
printGreen("\nAgent = {} \n".format(args.algo))
env_kwargs["action_repeat"] = args.action_repeat
# Random init position for button
env_kwargs["random_target"] = args.random_target
# If in simple continual scenario, then the target should be initialized randomly.
if args.simple_continual is True:
env_kwargs["random_target"] = True
# Allow up action
# env_kwargs["force_down"] = False
# allow multi-view
env_kwargs['multi_view'] = args.srl_model == "multi_view_srl"
parser = algo.customArguments(parser)
args = parser.parse_args()
args, env_kwargs = configureEnvAndLogFolder(args, env_kwargs, all_models)
args_dict = filterJSONSerializableObjects(vars(args))
# Save args
with open(LOG_DIR + "args.json", "w") as f:
json.dump(args_dict, f)
env_class = registered_env[args.env][0]
# env default kwargs
default_env_kwargs = {
k: v.default
for k, v in inspect.signature(env_class.__init__).parameters.items()
if v is not None
}
globals_env_param = sys.modules[env_class.__module__].getGlobals()
### HACK way to reset image shape !!
globals_env_param['RENDER_HEIGHT'] = img_shape[1]
globals_env_param['RENDER_WIDTH'] = img_shape[2]
globals_env_param['RELATIVE_POS'] = args.relative_pos
super_class = registered_env[args.env][1]
# reccursive search through all the super classes of the asked environment, in order to get all the arguments.
rec_super_class_lookup = {
dict_class: dict_super_class
for _, (dict_class, dict_super_class, _, _) in registered_env.items()
}
while super_class != SRLGymEnv:
assert super_class in rec_super_class_lookup, "Error: could not find super class of {}".format(super_class) + \
", are you sure \"registered_env\" is correctly defined?"
super_env_kwargs = {
k: v.default
for k, v in inspect.signature(
super_class.__init__).parameters.items() if v is not None
}
default_env_kwargs = {**super_env_kwargs, **default_env_kwargs}
globals_env_param = {
**sys.modules[super_class.__module__].getGlobals(),
**globals_env_param
}
super_class = rec_super_class_lookup[super_class]
# Print Variables
printYellow("Arguments:")
pprint(args_dict)
printYellow("Env Globals:")
pprint(
filterJSONSerializableObjects({
**globals_env_param,
**default_env_kwargs,
**env_kwargs
}))
# Save env params
saveEnvParams(globals_env_param, {**default_env_kwargs, **env_kwargs})
# Seed tensorflow, python and numpy random generator
set_global_seeds(args.seed)
# Augment the number of timesteps (when using mutliprocessing this number is not reached)
args.num_timesteps = int(1.1 * args.num_timesteps)
# Get the hyperparameter, if given (Hyperband)
hyperparams = {
param.split(":")[0]: param.split(":")[1]
for param in args.hyperparam
}
hyperparams = algo.parserHyperParam(hyperparams)
if args.load_rl_model_path is not None:
#use a small learning rate
print("use a small learning rate: {:f}".format(1.0e-4))
hyperparams["learning_rate"] = lambda f: f * 1.0e-4
# Train the agent
if args.load_rl_model_path is not None:
algo.setLoadPath(args.load_rl_model_path)
algo.train(args, callback, env_kwargs=env_kwargs, train_kwargs=hyperparams)
def main():
parser = argparse.ArgumentParser(
description="OpenAI RL Baselines Benchmark",
epilog=
'After the arguments are parsed, the rest are assumed to be arguments for'
+ ' rl_baselines.train')
parser.add_argument('--algo',
type=str,
default='ppo2',
help='OpenAI baseline to use',
choices=list(registered_rl.keys()))
parser.add_argument('--env',
type=str,
nargs='+',
default=["KukaButtonGymEnv-v0"],
help='environment ID(s)',
choices=list(registered_env.keys()))
parser.add_argument('--srl-model',
type=str,
nargs='+',
default=["raw_pixels"],
help='SRL model(s) to use',
choices=list(registered_srl.keys()))
parser.add_argument('--num-timesteps',
type=int,
default=1e6,
help='number of timesteps the baseline should run')
parser.add_argument('-v',
'--verbose',
action='store_true',
default=False,
help='Display baseline STDOUT')
parser.add_argument(
'--num-iteration',
type=int,
default=15,
help=
'number of time each algorithm should be run for each unique combination of environment '
+ ' and srl-model.')
parser.add_argument(
'--seed',
type=int,
default=0,
help=
'initial seed for each unique combination of environment and srl-model.'
)
parser.add_argument(
'--srl-config-file',
type=str,
default="config/srl_models.yaml",
help='Set the location of the SRL model path configuration.')
# returns the parsed arguments, and the rest are assumed to be arguments for rl_baselines.train
args, train_args = parser.parse_known_args()
# Sanity check
assert args.num_timesteps >= 1, "Error: --num-timesteps cannot be less than 1"
assert args.num_iteration >= 1, "Error: --num-iteration cannot be less than 1"
# Removing duplicates and sort
srl_models = list(set(args.srl_model))
envs = list(set(args.env))
srl_models.sort()
envs.sort()
# LOAD SRL models list
assert os.path.exists(args.srl_config_file), \
"Error: cannot load \"--srl-config-file {}\", file not found!".format(args.srl_config_file)
with open(args.srl_config_file, 'rb') as f:
all_models = yaml.load(f)
# Checking definition and presence of all requested srl_models
valid = True
for env in envs:
# validated the env definition
if env not in all_models:
printRed(
"Error: 'srl_models.yaml' missing definition for environment {}"
.format(env))
valid = False
continue # skip to the next env, this one is not valid
# checking log_folder for current env
missing_log = "log_folder" not in all_models[env]
if missing_log:
printRed(
"Error: 'srl_models.yaml' missing definition for log_folder in environment {}"
.format(env))
valid = False
# validate each model for the current env definition
for model in srl_models:
if registered_srl[model][0] == SRLType.ENVIRONMENT:
continue # not an srl model, skip to the next model
elif model not in all_models[env]:
printRed(
"Error: 'srl_models.yaml' missing srl_model {} for environment {}"
.format(model, env))
valid = False
elif (not missing_log) and (
not os.path.exists(all_models[env]["log_folder"] +
all_models[env][model])):
# checking presence of srl_model path, if and only if log_folder exists
printRed(
"Error: srl_model {} for environment {} was defined in ".
format(model, env) +
"'srl_models.yaml', however the file {} it was tagetting does not exist."
.format(all_models[env]["log_folder"] +
all_models[env][model]))
valid = False
assert valid, "Errors occured due to malformed 'srl_models.yaml', cannot continue."
# check that all the SRL_models can be run on all the environments
valid = True
for env in envs:
for model in srl_models:
if registered_srl[model][1] is not None:
found = False
for compatible_class in registered_srl[model][1]:
if issubclass(compatible_class, registered_env[env][0]):
found = True
break
if not found:
valid = False
printRed(
"Error: srl_model {}, is not compatible with the {} environment."
.format(model, env))
assert valid, "Errors occured due to an incompatible combination of srl_model and environment, cannot continue."
# the seeds used in training the baseline.
seeds = list(np.arange(args.num_iteration) + args.seed)
if args.verbose:
# None here means stdout of terminal for subprocess.call
stdout = None
else:
stdout = open(os.devnull, 'w')
printGreen("\nRunning {} benchmarks {} times...".format(
args.algo, args.num_iteration))
print("\nSRL-Models:\t{}".format(srl_models))
print("environments:\t{}".format(envs))
print("verbose:\t{}".format(args.verbose))
print("timesteps:\t{}".format(args.num_timesteps))
for model in srl_models:
for env in envs:
for i in range(args.num_iteration):
printGreen(
"\nIteration_num={} (seed: {}), Environment='{}', SRL-Model='{}'"
.format(i, seeds[i], env, model))
# redefine the parsed args for rl_baselines.train
loop_args = [
'--srl-model', model, '--seed',
str(seeds[i]), '--algo', args.algo, '--env', env,
'--num-timesteps',
str(int(args.num_timesteps)), '--srl-config-file',
args.srl_config_file
]
ok = subprocess.call(['python', '-m', 'rl_baselines.train'] +
train_args + loop_args,
stdout=stdout)
if ok != 0:
# throw the error down to the terminal
raise ChildProcessError(
"An error occured, error code: {}".format(ok))
def main():
parser = argparse.ArgumentParser(
description='Deteministic dataset generator for SRL training ' +
'(can be used for environment testing)')
parser.add_argument('--num-cpu',
type=int,
default=1,
help='number of cpu to run on')
parser.add_argument('--num-episode',
type=int,
default=50,
help='number of episode to run')
parser.add_argument(
'--save-path',
type=str,
default='srl_zoo/data/',
help='Folder where the environments will save the output')
parser.add_argument('--name',
type=str,
default='kuka_button',
help='Folder name for the output')
parser.add_argument('--env',
type=str,
default='KukaButtonGymEnv-v0',
help='The environment wanted',
choices=list(registered_env.keys()))
parser.add_argument('--display', action='store_true', default=False)
parser.add_argument('--no-record-data', action='store_true', default=False)
parser.add_argument(
'--max-distance',
type=float,
default=0.28,
help=
'Beyond this distance from the goal, the agent gets a negative reward')
parser.add_argument('-c',
'--continuous-actions',
action='store_true',
default=False)
parser.add_argument('--seed', type=int, default=0, help='the seed')
parser.add_argument(
'-f',
'--force',
action='store_true',
default=False,
help='Force the save, even if it overrides something else,' +
' including partial parts if they exist')
parser.add_argument('-r',
'--random-target',
action='store_true',
default=False,
help='Set the button to a random position')
parser.add_argument('--multi-view',
action='store_true',
default=False,
help='Set a second camera to the scene')
parser.add_argument(
'--shape-reward',
action='store_true',
default=False,
help='Shape the reward (reward = - distance) instead of a sparse reward'
)
parser.add_argument(
'--reward-dist',
action='store_true',
default=False,
help=
'Prints out the reward distribution when the dataset generation is finished'
)
parser.add_argument(
'--run-policy',
type=str,
default="random",
choices=VALID_POLICIES,
help='Policy to run for data collection ' +
'(random, localy pretrained ppo2, pretrained custom policy)')
parser.add_argument(
'--log-custom-policy',
type=str,
default='',
help='Logs of the custom pretained policy to run for data collection')
parser.add_argument(
'--latest',
action='store_true',
default=False,
help='load the latest learned model (location: args.log-custom-policy)'
)
parser.add_argument(
'-rgm',
'--replay-generative-model',
type=str,
default="",
choices=['vae'],
help=
'Generative model to replay for generating a dataset (for Continual Learning purposes)'
)
parser.add_argument(
'--log-generative-model',
type=str,
default='',
help='Logs of the custom pretained policy to run for data collection')
parser.add_argument(
'--ppo2-timesteps',
type=int,
default=1000,
help='number of timesteps to run PPO2 on before generating the dataset'
)
parser.add_argument(
'--toward-target-timesteps-proportion',
type=float,
default=0.0,
help=
"propotion of timesteps that use simply towards target policy, should be 0.0 to 1.0"
)
parser.add_argument(
'-sc',
'--simple-continual',
action='store_true',
default=False,
help=
'Simple red square target for task 1 of continual learning scenario. '
+ 'The task is: robot should reach the target.')
parser.add_argument(
'-cc',
'--circular-continual',
action='store_true',
default=False,
help='Blue square target for task 2 of continual learning scenario. ' +
'The task is: robot should turn in circle around the target.')
parser.add_argument(
'-sqc',
'--square-continual',
action='store_true',
default=False,
help='Green square target for task 3 of continual learning scenario. '
+ 'The task is: robot should turn in square around the target.')
parser.add_argument(
'--short-episodes',
action='store_true',
default=False,
help=
'Generate short episodes (only 10 contacts with the target allowed).')
parser.add_argument('--episode',
type=int,
default=-1,
help='Model saved at episode N that we want to load')
args = parser.parse_args()
assert (args.num_cpu >
0), "Error: number of cpu must be positive and non zero"
assert (args.max_distance >
0), "Error: max distance must be positive and non zero"
assert (args.num_episode >
0), "Error: number of episodes must be positive and non zero"
assert not args.reward_dist or not args.shape_reward, \
"Error: cannot display the reward distribution for continuous reward"
assert not(registered_env[args.env][3] is ThreadingType.NONE and args.num_cpu != 1), \
"Error: cannot have more than 1 CPU for the environment {}".format(args.env)
if args.num_cpu > args.num_episode:
args.num_cpu = args.num_episode
printYellow(
"num_cpu cannot be greater than num_episode, defaulting to {} cpus."
.format(args.num_cpu))
assert sum([args.simple_continual, args.circular_continual, args.square_continual]) <= 1, \
"For continual SRL and RL, please provide only one scenario at the time !"
assert not (args.log_custom_policy == '' and args.run_policy in ['walker', 'custom']), \
"If using a custom policy, please specify a valid log folder for loading it."
assert not (args.log_generative_model == '' and args.replay_generative_model == 'custom'), \
"If using a custom policy, please specify a valid log folder for loading it."
if not os.path.exists(args.save_path):
os.makedirs(args.save_path)
# this is done so seed 0 and 1 are different and not simply offset of the same datasets.
args.seed = np.random.RandomState(args.seed).randint(int(1e10))
# File exists, need to deal with it
if not args.no_record_data and os.path.exists(args.save_path + args.name):
assert args.force, "Error: save directory '{}' already exists".format(
args.save_path + args.name)
shutil.rmtree(args.save_path + args.name)
for part in glob.glob(args.save_path + args.name + "_part-[0-9]*"):
shutil.rmtree(part)
if not args.no_record_data:
# create the output
os.mkdir(args.save_path + args.name)
if args.num_cpu == 1:
env_thread(args, 0, partition=False)
else:
# try and divide into multiple processes, with an environment each
try:
jobs = []
for i in range(args.num_cpu):
process = multiprocessing.Process(target=env_thread,
args=(args, i, True))
jobs.append(process)
for j in jobs:
j.start()
try:
for j in jobs:
j.join()
except Exception as e:
printRed("Error: unable to join thread")
raise e
except Exception as e:
printRed("Error: unable to start thread")
raise e
if not args.no_record_data and args.num_cpu > 1:
# sleep 1 second, to avoid congruency issues from multiprocess (eg., files still writing)
time.sleep(1)
# get all the parts
file_parts = sorted(glob.glob(args.save_path + args.name +
"_part-[0-9]*"),
key=lambda a: int(a.split("-")[-1]))
# move the config files from any as they are identical
os.rename(file_parts[0] + "/dataset_config.json",
args.save_path + args.name + "/dataset_config.json")
os.rename(file_parts[0] + "/env_globals.json",
args.save_path + args.name + "/env_globals.json")
ground_truth = None
preprocessed_data = None
# used to convert the part record_id to the fused record_id
record_id = 0
for part in file_parts:
# sort the record names alphabetically, then numerically
records = sorted(glob.glob(part + "/record_[0-9]*"),
key=lambda a: int(a.split("_")[-1]))
record_id_start = record_id
for record in records:
os.renames(
record, args.save_path + args.name +
"/record_{:03d}".format(record_id))
record_id += 1
# fuse the npz files together, in the right order
if ground_truth is None:
# init
ground_truth = {}
preprocessed_data = {}
ground_truth_load = np.load(part + "/ground_truth.npz")
preprocessed_data_load = np.load(part +
"/preprocessed_data.npz")
for arr in ground_truth_load.files:
if arr == "images_path":
ground_truth[arr] = np.array([
convertImagePath(args, path, record_id_start)
for path in ground_truth_load[arr]
])
else:
ground_truth[arr] = ground_truth_load[arr]
for arr in preprocessed_data_load.files:
preprocessed_data[arr] = preprocessed_data_load[arr]
else:
ground_truth_load = np.load(part + "/ground_truth.npz")
preprocessed_data_load = np.load(part +
"/preprocessed_data.npz")
for arr in ground_truth_load.files:
if arr == "images_path":
sanitised_paths = np.array([
convertImagePath(args, path, record_id_start)
for path in ground_truth_load[arr]
])
ground_truth[arr] = np.concatenate(
(ground_truth[arr], sanitised_paths))
else:
ground_truth[arr] = np.concatenate(
(ground_truth[arr], ground_truth_load[arr]))
for arr in preprocessed_data_load.files:
preprocessed_data[arr] = np.concatenate(
(preprocessed_data[arr], preprocessed_data_load[arr]))
# remove the current part folder
shutil.rmtree(part)
# save the fused outputs
np.savez(args.save_path + args.name + "/ground_truth.npz",
**ground_truth)
np.savez(args.save_path + args.name + "/preprocessed_data.npz",
**preprocessed_data)
if args.reward_dist:
rewards, counts = np.unique(
np.load(args.save_path + args.name +
"/preprocessed_data.npz")['rewards'],
return_counts=True)
counts = [
"{:.2f}%".format(val * 100) for val in counts / np.sum(counts)
]
print("reward distribution:")
[
print(" ", reward, count)
for reward, count in list(zip(rewards, counts))
]
def main():
# Global variables for callback
parser = argparse.ArgumentParser(
description=
"Evaluation script for distillation from two teacher policies")
parser.add_argument('--seed',
type=int,
default=0,
help='random seed (default: 0)')
parser.add_argument('--env',
type=str,
help='environment ID',
default='OmnirobotEnv-v0',
choices=list(registered_env.keys()))
parser.add_argument(
'--episode_window',
type=int,
default=40,
help='Episode window for moving average plot (default: 40)')
parser.add_argument(
'--log-dir-teacher-one',
default='/tmp/gym/',
type=str,
help=
'directory to load an optmimal agent for task 1 (default: /tmp/gym)')
parser.add_argument(
'--log-dir-teacher-two',
default='/tmp/gym/',
type=str,
help=
'directory to load an optmimal agent for task 2 (default: /tmp/gym)')
parser.add_argument(
'--log-dir-student',
default='/tmp/gym/',
type=str,
help=
'directory to save the student agent logs and model (default: /tmp/gym)'
)
parser.add_argument(
'--srl-config-file-one',
type=str,
default="config/srl_models_one.yaml",
help='Set the location of the SRL model path configuration.')
parser.add_argument(
'--srl-config-file-two',
type=str,
default="config/srl_models_two.yaml",
help='Set the location of the SRL model path configuration.')
parser.add_argument(
'--epochs-distillation',
type=int,
default=30,
metavar='N',
help='number of epochs to train for distillation(default: 30)')
parser.add_argument(
'--distillation-training-set-size',
type=int,
default=-1,
help='Limit size (number of samples) of the training set (default: -1)'
)
parser.add_argument(
'--eval-tasks',
type=str,
nargs='+',
default=['cc', 'sqc', 'sc'],
help='A cross evaluation from the latest stored model to all tasks')
parser.add_argument(
'--continual-learning-labels',
type=str,
nargs=2,
metavar=('label_1', 'label_2'),
default=argparse.SUPPRESS,
help='Labels for the continual learning RL distillation task.')
parser.add_argument('--student-srl-model',
type=str,
default='raw_pixels',
choices=list(registered_srl.keys()),
help='SRL model to use for the student RL policy')
parser.add_argument(
'--epochs-teacher-datasets',
type=int,
default=30,
metavar='N',
help=
'number of epochs for generating both RL teacher datasets (default: 30)'
)
parser.add_argument(
'--num-iteration',
type=int,
default=1,
help='number of time each algorithm should be run the eval (N seeds).')
parser.add_argument(
'--eval-episode-window',
type=int,
default=400,
metavar='N',
help=
'Episode window for saving each policy checkpoint for future distillation(default: 100)'
)
args, unknown = parser.parse_known_args()
if 'continual_learning_labels' in args:
assert args.continual_learning_labels[0] in CONTINUAL_LEARNING_LABELS and args.continual_learning_labels[1] \
in CONTINUAL_LEARNING_LABELS, "Please specify a valid Continual learning label to each dataset to be " \
"used for RL distillation !"
print(args.continual_learning_labels)
assert os.path.exists(args.srl_config_file_one), \
"Error: cannot load \"--srl-config-file {}\", file not found!".format(args.srl_config_file_one)
assert os.path.exists(args.srl_config_file_two), \
"Error: cannot load \"--srl-config-file {}\", file not found!".format(args.srl_config_file_two)
if not (args.log_dir_teacher_one == "None"):
assert os.path.exists(args.log_dir_teacher_one), \
"Error: cannot load \"--srl-config-file {}\", file not found!".format(args.log_dir_teacher_one)
assert os.path.exists(args.log_dir_teacher_two), \
"Error: cannot load \"--srl-config-file {}\", file not found!".format(args.srl_config_file_two)
teacher_pro = args.log_dir_teacher_one
teacher_learn = args.log_dir_teacher_two
# The output path generate from the
teacher_pro_data = args.continual_learning_labels[0] + '/'
teacher_learn_data = args.continual_learning_labels[1] + '/'
merge_path = "data/on_policy_merged"
print(teacher_pro_data, teacher_learn_data)
episodes, policy_path = allPolicy(teacher_learn)
rewards_at_episode = {}
episodes_to_test = [
e for e in episodes if (int(e) < 2000 and int(e) % 200 == 0) or (
int(e) > 2000 and int(e) % 1000 == 0)
]
# generate data from Professional teacher
printYellow("\nGenerating on policy for optimal teacher: " +
args.continual_learning_labels[0])
if not (args.log_dir_teacher_one == "None"):
OnPolicyDatasetGenerator(teacher_pro,
args.continual_learning_labels[0] + '_copy/',
task_id=args.continual_learning_labels[0],
num_eps=args.epochs_teacher_datasets,
episode=-1,
env_name=args.env)
print("Eval on eps list: ", episodes_to_test)
for eps in episodes_to_test:
student_path = args.log_dir_student
printBlue("\n\nEvaluation at episode " + str(eps))
if not (args.log_dir_teacher_one == "None"):
# Use a copy of the optimal teacher
ok = subprocess.call([
'cp', '-r',
'data/' + args.continual_learning_labels[0] + '_copy/',
'data/' + teacher_pro_data, '-f'
])
assert ok == 0
time.sleep(2)
# Generate data from learning teacher
printYellow("\nGenerating on-policy data from the optimal teacher: " +
args.continual_learning_labels[1])
OnPolicyDatasetGenerator(teacher_learn,
teacher_learn_data,
task_id=args.continual_learning_labels[1],
episode=eps,
num_eps=args.epochs_teacher_datasets,
env_name=args.env)
if args.log_dir_teacher_one == "None":
merge_path = 'data/' + teacher_learn_data
ok = subprocess.call(
['cp', '-r', merge_path, 'srl_zoo/data/', '-f'])
else:
# merge the data
mergeData('data/' + teacher_pro_data,
'data/' + teacher_learn_data,
merge_path,
force=True)
ok = subprocess.call(
['cp', '-r', 'data/on_policy_merged/', 'srl_zoo/data/', '-f'])
assert ok == 0
time.sleep(2)
# Train a policy with distillation on the merged teacher's datasets
trainStudent('srl_zoo/' + merge_path,
args.continual_learning_labels[1],
yaml_file=args.srl_config_file_one,
log_dir=args.log_dir_student,
srl_model=args.student_srl_model,
env_name=args.env,
training_size=args.distillation_training_set_size,
epochs=args.epochs_distillation)
student_path += args.env + '/' + args.student_srl_model + "/distillation/"
latest_student_path = max([
student_path + "/" + d for d in os.listdir(student_path)
if os.path.isdir(student_path + "/" + d)
],
key=os.path.getmtime) + '/'
rewards = {}
printRed("\nSaving the student at path: " + latest_student_path)
for task_label in ["-sc", "-cc"]:
rewards[task_label] = []
for seed_i in range(args.num_iteration):
printYellow("\nEvaluating student on task: " + task_label +
" for seed: " + str(seed_i))
command_line_enjoy_student = [
'python', '-m', 'replay.enjoy_baselines',
'--num-timesteps', '251', '--log-dir', latest_student_path,
task_label, "--seed",
str(seed_i)
]
ok = subprocess.check_output(command_line_enjoy_student)
ok = ok.decode('utf-8')
str_before = "Mean reward: "
str_after = "\npybullet"
idx_before = ok.find(str_before) + len(str_before)
idx_after = ok.find(str_after)
seed_reward = float(ok[idx_before:idx_after])
rewards[task_label].append(seed_reward)
print("rewards at eps ", eps, ": ", rewards)
rewards_at_episode[eps] = rewards
print("All rewards: ", rewards_at_episode)
json_dict = json.dumps(rewards_at_episode)
json_dict_name = \
args.log_dir_student + "/reward_at_episode_" + datetime.datetime.now().strftime("%y-%m-%d_%Hh%M_%S") + '.json'
f = open(json_dict_name, "w")
f.write(json_dict)
f.close()
printRed("\nSaving the evalation at path: " + json_dict_name)
def main():
# Global variables for callback
global ENV_NAME, ALGO, ALGO_NAME, LOG_INTERVAL, VISDOM_PORT, viz
global SAVE_INTERVAL, EPISODE_WINDOW, MIN_EPISODES_BEFORE_SAVE
parser = argparse.ArgumentParser(description="Train script for RL algorithms")
parser.add_argument('--algo', default='ppo2', choices=list(registered_rl.keys()), help='RL algo to use',
type=str)
parser.add_argument('--env', type=str, help='environment ID', default='KukaButtonGymEnv-v0',
choices=list(registered_env.keys()))
parser.add_argument('--seed', type=int, default=0, help='random seed (default: 0)')
parser.add_argument('--episode_window', type=int, default=40,
help='Episode window for moving average plot (default: 40)')
parser.add_argument('--log-dir', default='/tmp/gym/', type=str,
help='directory to save agent logs and model (default: /tmp/gym)')
parser.add_argument('--num-timesteps', type=int, default=int(1e6))
parser.add_argument('--srl-model', type=str, default='raw_pixels', choices=list(registered_srl.keys()),
help='SRL model to use')
parser.add_argument('--num-stack', type=int, default=1, help='number of frames to stack (default: 1)')
parser.add_argument('--action-repeat', type=int, default=1,
help='number of times an action will be repeated (default: 1)')
parser.add_argument('--port', type=int, default=8097, help='visdom server port (default: 8097)')
parser.add_argument('--no-vis', action='store_true', default=False, help='disables visdom visualization')
parser.add_argument('--shape-reward', action='store_true', default=False,
help='Shape the reward (reward = - distance) instead of a sparse reward')
parser.add_argument('-c', '--continuous-actions', action='store_true', default=False)
parser.add_argument('-joints', '--action-joints', action='store_true', default=False,
help='set actions to the joints of the arm directly, instead of inverse kinematics')
parser.add_argument('-r', '--random-target', action='store_true', default=False,
help='Set the button to a random position')
parser.add_argument('--srl-config-file', type=str, default="config/srl_models.yaml",
help='Set the location of the SRL model path configuration.')
parser.add_argument('--hyperparam', type=str, nargs='+', default=[])
parser.add_argument('--min-episodes-save', type=int, default=100,
help="Min number of episodes before saving best model")
parser.add_argument('--latest', action='store_true', default=False,
help='load the latest learned model (location:srl_zoo/logs/DatasetName/)')
parser.add_argument('--load-rl-model-path', type=str, default=None,
help="load the trained RL model, should be with the same algorithm type")
# Ignore unknown args for now
args, unknown = parser.parse_known_args()
env_kwargs = {}
# LOAD SRL models list
assert os.path.exists(args.srl_config_file), \
"Error: cannot load \"--srl-config-file {}\", file not found!".format(args.srl_config_file)
with open(args.srl_config_file, 'rb') as f:
all_models = yaml.load(f)
# Sanity check
assert args.episode_window >= 1, "Error: --episode_window cannot be less than 1"
assert args.num_timesteps >= 1, "Error: --num-timesteps cannot be less than 1"
assert args.num_stack >= 1, "Error: --num-stack cannot be less than 1"
assert args.action_repeat >= 1, "Error: --action-repeat cannot be less than 1"
assert 0 <= args.port < 65535, "Error: invalid visdom port number {}, ".format(args.port) + \
"port number must be an unsigned 16bit number [0,65535]."
assert registered_srl[args.srl_model][0] == SRLType.ENVIRONMENT or args.env in all_models, \
"Error: the environment {} has no srl_model defined in 'srl_models.yaml'. Cannot continue.".format(args.env)
# check that all the SRL_model can be run on the environment
if registered_srl[args.srl_model][1] is not None:
found = False
for compatible_class in registered_srl[args.srl_model][1]:
if issubclass(compatible_class, registered_env[args.env][0]):
found = True
break
assert found, "Error: srl_model {}, is not compatible with the {} environment.".format(args.srl_model, args.env)
ENV_NAME = args.env
ALGO_NAME = args.algo
VISDOM_PORT = args.port
EPISODE_WINDOW = args.episode_window
MIN_EPISODES_BEFORE_SAVE = args.min_episodes_save
if args.no_vis:
viz = False
algo_class, algo_type, action_type = registered_rl[args.algo]
algo = algo_class()
ALGO = algo
# if callback frequency needs to be changed
LOG_INTERVAL = algo.LOG_INTERVAL
SAVE_INTERVAL = algo.SAVE_INTERVAL
if not args.continuous_actions and ActionType.DISCRETE not in action_type:
raise ValueError(args.algo + " does not support discrete actions, please use the '--continuous-actions' " +
"(or '-c') flag.")
if args.continuous_actions and ActionType.CONTINUOUS not in action_type:
raise ValueError(args.algo + " does not support continuous actions, please remove the '--continuous-actions' " +
"(or '-c') flag.")
env_kwargs["is_discrete"] = not args.continuous_actions
printGreen("\nAgent = {} \n".format(args.algo))
env_kwargs["action_repeat"] = args.action_repeat
# Random init position for button
env_kwargs["random_target"] = args.random_target
# Allow up action
# env_kwargs["force_down"] = False
# allow multi-view
env_kwargs['multi_view'] = args.srl_model == "multi_view_srl"
parser = algo.customArguments(parser)
args = parser.parse_args()
args, env_kwargs = configureEnvAndLogFolder(args, env_kwargs, all_models)
args_dict = filterJSONSerializableObjects(vars(args))
# Save args
with open(LOG_DIR + "args.json", "w") as f:
json.dump(args_dict, f)
env_class = registered_env[args.env][0]
# env default kwargs
default_env_kwargs = {k: v.default
for k, v in inspect.signature(env_class.__init__).parameters.items()
if v is not None}
globals_env_param = sys.modules[env_class.__module__].getGlobals()
super_class = registered_env[args.env][1]
# reccursive search through all the super classes of the asked environment, in order to get all the arguments.
rec_super_class_lookup = {dict_class: dict_super_class for _, (dict_class, dict_super_class, _, _) in
registered_env.items()}
while super_class != SRLGymEnv:
assert super_class in rec_super_class_lookup, "Error: could not find super class of {}".format(super_class) + \
", are you sure \"registered_env\" is correctly defined?"
super_env_kwargs = {k: v.default
for k, v in inspect.signature(super_class.__init__).parameters.items()
if v is not None}
default_env_kwargs = {**super_env_kwargs, **default_env_kwargs}
globals_env_param = {**sys.modules[super_class.__module__].getGlobals(), **globals_env_param}
super_class = rec_super_class_lookup[super_class]
# Print Variables
printYellow("Arguments:")
pprint(args_dict)
printYellow("Env Globals:")
pprint(filterJSONSerializableObjects({**globals_env_param, **default_env_kwargs, **env_kwargs}))
# Save env params
saveEnvParams(globals_env_param, {**default_env_kwargs, **env_kwargs})
# Seed tensorflow, python and numpy random generator
set_global_seeds(args.seed)
# Augment the number of timesteps (when using mutliprocessing this number is not reached)
args.num_timesteps = int(1.1 * args.num_timesteps)
# Get the hyperparameter, if given (Hyperband)
hyperparams = {param.split(":")[0]: param.split(":")[1] for param in args.hyperparam}
hyperparams = algo.parserHyperParam(hyperparams)
if args.load_rl_model_path is not None:
#use a small learning rate
print("use a small learning rate: {:f}".format(1.0e-4))
hyperparams["learning_rate"] = lambda f: f * 1.0e-4
# Train the agent
if args.load_rl_model_path is not None:
algo.setLoadPath(args.load_rl_model_path)
algo.train(args, callback, env_kwargs=env_kwargs, train_kwargs=hyperparams)
