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import os
import pickle
import torch
from functools import partial
from tensorboardX import SummaryWriter
from torch.utils.data import DataLoader
from typing import Union, Optional, List, Any, Tuple, Dict
from ding.worker import BaseLearner, BaseSerialCommander, InteractionSerialEvaluator, create_serial_collector
from ding.config import read_config, compile_config
from ding.utils import set_pkg_seed
from ding.envs import get_vec_env_setting, create_env_manager
from ding.policy.common_utils import default_preprocess_learn
from ding.policy import create_policy
from ding.utils.data.dataset import BCODataset
from ding.world_model.idm import InverseDynamicsModel
def load_expertdata(data: Dict[str, torch.Tensor]) -> BCODataset:
"""
loading from demonstration data, which only have obs and next_obs
action need to be inferred from Inverse Dynamics Model
"""
post_data = list()
for episode in range(len(data)):
for transition in data[episode]:
transition['episode_id'] = episode
post_data.append(transition)
post_data = default_preprocess_learn(post_data)
return BCODataset(
{
'obs': torch.cat((post_data['obs'], post_data['next_obs']), 1),
'episode_id': post_data['episode_id'],
'action': post_data['action']
}
)
def load_agentdata(data) -> BCODataset:
"""
loading from policy data, which only have obs and next_obs as features and action as label
"""
post_data = list()
for episode in range(len(data)):
for transition in data[episode]:
transition['episode_id'] = episode
post_data.append(transition)
post_data = default_preprocess_learn(post_data)
return BCODataset(
{
'obs': torch.cat((post_data['obs'], post_data['next_obs']), 1),
'action': post_data['action'],
'episode_id': post_data['episode_id']
}
)
def serial_pipeline_bco(
input_cfg: Union[str, Tuple[dict, dict]],
expert_cfg: Union[str, Tuple[dict, dict]],
seed: int = 0,
env_setting: Optional[List[Any]] = None,
model: Optional[torch.nn.Module] = None,
expert_model: Optional[torch.nn.Module] = None,
# model: Optional[torch.nn.Module] = None,
max_train_iter: Optional[int] = int(1e10),
max_env_step: Optional[int] = int(1e10),
) -> None:
if isinstance(input_cfg, str):
cfg, create_cfg = read_config(input_cfg)
expert_cfg, expert_create_cfg = read_config(expert_cfg)
else:
cfg, create_cfg = input_cfg
expert_cfg, expert_create_cfg = expert_cfg
create_cfg.policy.type = create_cfg.policy.type + '_command'
expert_create_cfg.policy.type = expert_create_cfg.policy.type + '_command'
env_fn = None if env_setting is None else env_setting[0]
cfg = compile_config(cfg, seed=seed, env=env_fn, auto=True, create_cfg=create_cfg, save_cfg=True)
expert_cfg = compile_config(
expert_cfg, seed=seed, env=env_fn, auto=True, create_cfg=expert_create_cfg, save_cfg=True
)
# Random seed
set_pkg_seed(cfg.seed, use_cuda=cfg.policy.cuda)
# Create main components: env, policy
if env_setting is None:
env_fn, collector_env_cfg, evaluator_env_cfg = get_vec_env_setting(cfg.env)
else:
env_fn, collector_env_cfg, evaluator_env_cfg = env_setting
# Generate Expert Data
if cfg.policy.collect.model_path is None:
with open(cfg.policy.collect.data_path, 'rb') as f:
data = pickle.load(f)
expert_learn_dataset = load_expertdata(data)
else:
expert_policy = create_policy(expert_cfg.policy, model=expert_model, enable_field=['collect'])
expert_collector_env = create_env_manager(
expert_cfg.env.manager, [partial(env_fn, cfg=c) for c in collector_env_cfg]
)
expert_collector_env.seed(expert_cfg.seed)
expert_policy.collect_mode.load_state_dict(torch.load(cfg.policy.collect.model_path, map_location='cpu'))
expert_collector = create_serial_collector(
cfg.policy.collect.collector, # for episode collector
env=expert_collector_env,
policy=expert_policy.collect_mode,
exp_name=expert_cfg.exp_name
)
# if expert policy is sac, eps kwargs is unexpected
if cfg.policy.continuous:
expert_data = expert_collector.collect(n_episode=100)
else:
policy_kwargs = {'eps': 0}
expert_data = expert_collector.collect(n_episode=100, policy_kwargs=policy_kwargs)
expert_learn_dataset = load_expertdata(expert_data)
expert_collector.reset_policy(expert_policy.collect_mode)
# Main components
tb_logger = SummaryWriter(os.path.join('./{}/log/'.format(cfg.exp_name), 'serial'))
policy = create_policy(cfg.policy, model=model, enable_field=['learn', 'collect', 'eval', 'command'])
learner = BaseLearner(cfg.policy.learn.learner, policy.learn_mode, tb_logger, exp_name=cfg.exp_name)
collector_env = create_env_manager(cfg.env.manager, [partial(env_fn, cfg=c) for c in collector_env_cfg])
evaluator_env = create_env_manager(cfg.env.manager, [partial(env_fn, cfg=c) for c in evaluator_env_cfg])
collector_env.seed(cfg.seed)
evaluator_env.seed(cfg.seed, dynamic_seed=False)
collector = create_serial_collector(
cfg.policy.collect.collector,
env=collector_env,
policy=policy.collect_mode,
tb_logger=tb_logger,
exp_name=cfg.exp_name
)
evaluator = InteractionSerialEvaluator(
cfg.policy.eval.evaluator, evaluator_env, policy.eval_mode, tb_logger, exp_name=cfg.exp_name
)
commander = BaseSerialCommander(
cfg.policy.other.commander, learner, collector, evaluator, None, policy=policy.command_mode
)
learned_model = InverseDynamicsModel(
cfg.policy.model.obs_shape, cfg.policy.model.action_shape, cfg.bco.model.idm_encoder_hidden_size_list,
cfg.bco.model.action_space
)
# ==========
# Main loop
# ==========
learner.call_hook('before_run')
collect_episode = int(cfg.policy.collect.n_episode * cfg.bco.alpha)
init_episode = True
while True:
collect_kwargs = commander.step()
# Evaluate policy performance
if evaluator.should_eval(learner.train_iter):
stop, reward = evaluator.eval(learner.save_checkpoint, learner.train_iter, collector.envstep)
if stop:
break
if init_episode:
new_data = collector.collect(
n_episode=cfg.policy.collect.n_episode, train_iter=learner.train_iter, policy_kwargs=collect_kwargs
)
init_episode = False
else:
new_data = collector.collect(
n_episode=collect_episode, train_iter=learner.train_iter, policy_kwargs=collect_kwargs
)
learn_dataset = load_agentdata(new_data)
learn_dataloader = DataLoader(learn_dataset, cfg.bco.learn.idm_batch_size)
for i, train_data in enumerate(learn_dataloader):
idm_loss = learned_model.train(
train_data,
cfg.bco.learn.idm_train_epoch,
cfg.bco.learn.idm_learning_rate,
cfg.bco.learn.idm_weight_decay,
)
# tb_logger.add_scalar("learner_iter/idm_loss", idm_loss, learner.train_iter)
# tb_logger.add_scalar("learner_step/idm_loss", idm_loss, collector.envstep)
# Generate state transitions from demonstrated state trajectories by IDM
expert_action_data = learned_model.predict_action(expert_learn_dataset.obs)['action']
post_expert_dataset = BCODataset(
{
# next_obs are deleted
'obs': expert_learn_dataset.obs[:, 0:int(expert_learn_dataset.obs.shape[1] // 2)],
'action': expert_action_data,
'expert_action': expert_learn_dataset.action
}
) # post_expert_dataset: Only obs and action are reserved for BC. next_obs are deleted
expert_learn_dataloader = DataLoader(post_expert_dataset, cfg.policy.learn.batch_size)
# Improve policy using BC
for epoch in range(cfg.policy.learn.train_epoch):
for i, train_data in enumerate(expert_learn_dataloader):
learner.train(train_data, collector.envstep)
if cfg.policy.learn.lr_decay:
learner.policy.get_attribute('lr_scheduler').step()
if collector.envstep >= max_env_step or learner.train_iter >= max_train_iter:
break
# Learner's after_run hook.
learner.call_hook('after_run')
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