def callback(_locals, _globals):
"""
Callback called at each step (for DQN an others) or after n steps (see ACER or PPO2)
:param _locals: (dict)
:param _globals: (dict)
"""
global win, win_smooth, win_episodes, n_steps, viz, params_saved, best_mean_reward
# Create vizdom object only if needed
if viz is None:
viz = Visdom(port=VISDOM_PORT)
is_es = registered_rl[ALGO_NAME][1] == AlgoType.EVOLUTION_STRATEGIES
# Save RL agent parameters
if not params_saved:
# Filter locals
params = filterJSONSerializableObjects(_locals)
with open(LOG_DIR + "rl_locals.json", "w") as f:
json.dump(params, f)
params_saved = True
# Save the RL model if it has improved
if (n_steps + 1) % SAVE_INTERVAL == 0:
# Evaluate network performance
ok, mean_reward = computeMeanReward(LOG_DIR, N_EPISODES_EVAL, is_es=is_es, return_n_episodes=True)
if ok:
# Unpack mean reward and number of episodes
mean_reward, n_episodes = mean_reward
print(
"Best mean reward: {:.2f} - Last mean reward per episode: {:.2f}".format(best_mean_reward, mean_reward))
else:
# Not enough episode
mean_reward = -10000
n_episodes = 0
# Save Best model
if mean_reward > best_mean_reward and n_episodes >= MIN_EPISODES_BEFORE_SAVE:
# Try saving the running average (only valid for mlp policy)
try:
if 'env' in _locals:
_locals['env'].save_running_average(LOG_DIR)
else:
_locals['self'].env.save_running_average(LOG_DIR)
except AttributeError:
pass
best_mean_reward = mean_reward
printGreen("Saving new best model")
ALGO.save(LOG_DIR + ALGO_NAME + "_model.pkl", _locals)
# Plots in visdom
if viz and (n_steps + 1) % LOG_INTERVAL == 0:
win = timestepsPlot(viz, win, LOG_DIR, ENV_NAME, ALGO_NAME, bin_size=1, smooth=0, title=PLOT_TITLE, is_es=is_es)
win_smooth = timestepsPlot(viz, win_smooth, LOG_DIR, ENV_NAME, ALGO_NAME, title=PLOT_TITLE + " smoothed",
is_es=is_es)
win_episodes = episodePlot(viz, win_episodes, LOG_DIR, ENV_NAME, ALGO_NAME, window=EPISODE_WINDOW,
title=PLOT_TITLE + " [Episodes]", is_es=is_es)
n_steps += 1
return True
def saveEnvParams(kuka_env_globals, env_kwargs):
"""
:param kuka_env_globals: (dict)
:param env_kwargs: (dict) The extra arguments for the environment
"""
params = filterJSONSerializableObjects({**kuka_env_globals, **env_kwargs})
with open(LOG_DIR + "env_globals.json", "w") as f:
json.dump(params, f)
def __init__(self,
name,
max_dist,
state_dim=-1,
globals_=None,
learn_every=3,
learn_states=False,
path='data/',
relative_pos=False):
super(EpisodeSaver, self).__init__()
self.name = name
self.data_folder = path + name
self.path = path
try:
os.makedirs(self.data_folder)
except OSError:
printYellow("Folder already exist")
self.actions = []
self.actions_proba = []
self.rewards = []
self.images = []
self.target_positions = []
self.episode_starts = []
self.ground_truth_states = []
self.images_path = []
self.episode_step = 0
self.episode_idx = -1
self.episode_folder = None
self.episode_success = False
self.state_dim = state_dim
self.learn_states = learn_states
self.learn_every = learn_every # Every n episodes, learn a state representation
self.srl_model_path = ""
self.n_steps = 0
self.max_steps = 10000
self.dataset_config = {
'relative_pos': relative_pos,
'max_dist': str(max_dist)
}
with open("{}/dataset_config.json".format(self.data_folder), "w") as f:
json.dump(self.dataset_config, f)
if globals_ is not None:
# Save environments parameters
with open("{}/env_globals.json".format(self.data_folder),
"w") as f:
json.dump(filterJSONSerializableObjects(globals_), f)
if self.learn_states:
self.socket_client = SRLClient(self.name)
self.socket_client.waitForServer()
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():
# 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)
