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from typing import Union, Optional, List, Any, Tuple
import os
import torch
import numpy as np
from ditk import logging
from functools import partial
from tensorboardX import SummaryWriter
from copy import deepcopy
from ding.envs import get_vec_env_setting, create_env_manager
from ding.worker import BaseLearner, InteractionSerialEvaluator, BaseSerialCommander, create_buffer, \
create_serial_collector
from ding.config import read_config, compile_config
from ding.policy import create_policy
from ding.utils import set_pkg_seed
from .utils import random_collect, mark_not_expert
def serial_pipeline_dqfd(
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,
max_train_iter: Optional[int] = int(1e10),
max_env_step: Optional[int] = int(1e10),
) -> 'Policy': # noqa
"""
Overview:
Serial pipeline dqfd entry: we create this serial pipeline in order to\
implement dqfd in DI-engine. For now, we support the following envs\
Cartpole, Lunarlander, Pong, Spaceinvader. The demonstration\
data come from the expert model. We use a well-trained model to \
generate demonstration data online
Arguments:
- input_cfg (:obj:`Union[str, Tuple[dict, dict]]`): Config in dict type. \
``str`` type means config file path. \
``Tuple[dict, dict]`` type means [user_config, create_cfg].
- seed (:obj:`int`): Random seed.
- env_setting (:obj:`Optional[List[Any]]`): A list with 3 elements: \
``BaseEnv`` subclass, collector env config, and evaluator env config.
- model (:obj:`Optional[torch.nn.Module]`): Instance of torch.nn.Module.
- expert_model (:obj:`Optional[torch.nn.Module]`): Instance of torch.nn.Module.\
The default model is DQN(**cfg.policy.model)
- max_train_iter (:obj:`Optional[int]`): Maximum policy update iterations in training.
- max_env_step (:obj:`Optional[int]`): Maximum collected environment interaction steps.
Returns:
- policy (:obj:`Policy`): Converged policy.
"""
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 = deepcopy(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
)
# 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
collector_env = create_env_manager(cfg.env.manager, [partial(env_fn, cfg=c) for c in collector_env_cfg])
expert_collector_env = create_env_manager(
expert_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])
expert_collector_env.seed(cfg.seed)
collector_env.seed(cfg.seed)
evaluator_env.seed(cfg.seed, dynamic_seed=False)
expert_policy = create_policy(expert_cfg.policy, model=expert_model, enable_field=['collect', 'command'])
set_pkg_seed(cfg.seed, use_cuda=cfg.policy.cuda)
policy = create_policy(cfg.policy, model=model, enable_field=['learn', 'collect', 'eval', 'command'])
expert_policy.collect_mode.load_state_dict(torch.load(cfg.policy.collect.model_path, map_location='cpu'))
# Create worker components: learner, collector, evaluator, replay buffer, commander.
tb_logger = SummaryWriter(os.path.join('./{}/log/'.format(cfg.exp_name), 'serial'))
learner = BaseLearner(cfg.policy.learn.learner, policy.learn_mode, tb_logger, exp_name=cfg.exp_name)
collector = create_serial_collector(
cfg.policy.collect.collector,
env=collector_env,
policy=policy.collect_mode,
tb_logger=tb_logger,
exp_name=cfg.exp_name
)
expert_collector = create_serial_collector(
expert_cfg.policy.collect.collector,
env=expert_collector_env,
policy=expert_policy.collect_mode,
tb_logger=tb_logger,
exp_name=expert_cfg.exp_name
)
evaluator = InteractionSerialEvaluator(
cfg.policy.eval.evaluator, evaluator_env, policy.eval_mode, tb_logger, exp_name=cfg.exp_name
)
replay_buffer = create_buffer(cfg.policy.other.replay_buffer, tb_logger=tb_logger, exp_name=cfg.exp_name)
commander = BaseSerialCommander(
cfg.policy.other.commander, learner, collector, evaluator, replay_buffer, policy.command_mode
)
expert_commander = BaseSerialCommander(
expert_cfg.policy.other.commander, learner, expert_collector, evaluator, replay_buffer,
expert_policy.command_mode
) # we create this to avoid the issue of eps, this is an issue due to the sample collector part.
expert_collect_kwargs = expert_commander.step()
if 'eps' in expert_collect_kwargs:
expert_collect_kwargs['eps'] = -1
# ==========
# Main loop
# ==========
# Learner's before_run hook.
learner.call_hook('before_run')
if cfg.policy.learn.expert_replay_buffer_size != 0: # for ablation study
dummy_variable = deepcopy(cfg.policy.other.replay_buffer)
dummy_variable['replay_buffer_size'] = cfg.policy.learn.expert_replay_buffer_size
expert_buffer = create_buffer(dummy_variable, tb_logger=tb_logger, exp_name=cfg.exp_name)
expert_data = expert_collector.collect(
n_sample=cfg.policy.learn.expert_replay_buffer_size, policy_kwargs=expert_collect_kwargs
)
for i in range(len(expert_data)):
expert_data[i]['is_expert'] = 1 # set is_expert flag(expert 1, agent 0)
expert_buffer.push(expert_data, cur_collector_envstep=0)
for _ in range(cfg.policy.learn.per_train_iter_k): # pretrain
if evaluator.should_eval(learner.train_iter):
stop, reward = evaluator.eval(learner.save_checkpoint, learner.train_iter, collector.envstep)
if stop:
break
# Learn policy from collected data
# Expert_learner will train ``update_per_collect == 1`` times in one iteration.
train_data = expert_buffer.sample(learner.policy.get_attribute('batch_size'), learner.train_iter)
learner.train(train_data, collector.envstep)
if learner.policy.get_attribute('priority'):
expert_buffer.update(learner.priority_info)
learner.priority_info = {}
# Accumulate plenty of data at the beginning of training.
if cfg.policy.get('random_collect_size', 0) > 0:
random_collect(
cfg.policy, policy, collector, collector_env, commander, replay_buffer, postprocess_data_fn=mark_not_expert
)
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
# Collect data by default config n_sample/n_episode
new_data = collector.collect(train_iter=learner.train_iter, policy_kwargs=collect_kwargs)
for i in range(len(new_data)):
new_data[i]['is_expert'] = 0 # set is_expert flag(expert 1, agent 0)
replay_buffer.push(new_data, cur_collector_envstep=collector.envstep)
# Learn policy from collected data
for i in range(cfg.policy.learn.update_per_collect):
if cfg.policy.learn.expert_replay_buffer_size != 0:
# Learner will train ``update_per_collect`` times in one iteration.
# The hyperparameter pho, the demo ratio, control the propotion of data coming\
# from expert demonstrations versus from the agent's own experience.
stats = np.random.choice(
(learner.policy.get_attribute('batch_size')), size=(learner.policy.get_attribute('batch_size'))
) < (
learner.policy.get_attribute('batch_size')
) * cfg.policy.collect.pho # torch.rand((learner.policy.get_attribute('batch_size')))\
# < cfg.policy.collect.pho
expert_batch_size = stats[stats].shape[0]
demo_batch_size = (learner.policy.get_attribute('batch_size')) - expert_batch_size
train_data = replay_buffer.sample(demo_batch_size, learner.train_iter)
train_data_demonstration = expert_buffer.sample(expert_batch_size, learner.train_iter)
if train_data is None:
# It is possible that replay buffer's data count is too few to train ``update_per_collect`` times
logging.warning(
"Replay buffer's data can only train for {} steps. ".format(i) +
"You can modify data collect config, e.g. increasing n_sample, n_episode."
)
break
train_data = train_data + train_data_demonstration
learner.train(train_data, collector.envstep)
if learner.policy.get_attribute('priority'):
# When collector, set replay_buffer_idx and replay_unique_id for each data item, priority = 1.\
# When learner, assign priority for each data item according their loss
learner.priority_info_agent = deepcopy(learner.priority_info)
learner.priority_info_expert = deepcopy(learner.priority_info)
learner.priority_info_agent['priority'] = learner.priority_info['priority'][0:demo_batch_size]
learner.priority_info_agent['replay_buffer_idx'] = learner.priority_info['replay_buffer_idx'][
0:demo_batch_size]
learner.priority_info_agent['replay_unique_id'] = learner.priority_info['replay_unique_id'][
0:demo_batch_size]
learner.priority_info_expert['priority'] = learner.priority_info['priority'][demo_batch_size:]
learner.priority_info_expert['replay_buffer_idx'] = learner.priority_info['replay_buffer_idx'][
demo_batch_size:]
learner.priority_info_expert['replay_unique_id'] = learner.priority_info['replay_unique_id'][
demo_batch_size:]
# Expert data and demo data update their priority separately.
replay_buffer.update(learner.priority_info_agent)
expert_buffer.update(learner.priority_info_expert)
else:
# Learner will train ``update_per_collect`` times in one iteration.
train_data = replay_buffer.sample(learner.policy.get_attribute('batch_size'), learner.train_iter)
if train_data is None:
# It is possible that replay buffer's data count is too few to train ``update_per_collect`` times
logging.warning(
"Replay buffer's data can only train for {} steps. ".format(i) +
"You can modify data collect config, e.g. increasing n_sample, n_episode."
)
break
learner.train(train_data, collector.envstep)
if learner.policy.get_attribute('priority'):
replay_buffer.update(learner.priority_info)
if collector.envstep >= max_env_step or learner.train_iter >= max_train_iter:
break
# Learner's after_run hook.
learner.call_hook('after_run')
return policy
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