File size: 10,374 Bytes
079c32c |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 |
from typing import Union, Optional, List, Any, Tuple
import os
import torch
from ditk import logging
import copy
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, mark_warm_up
def serial_pipeline_td3_vae(
input_cfg: Union[str, Tuple[dict, dict]],
seed: int = 0,
env_setting: Optional[List[Any]] = None,
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 entry for VAE latent action.
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.
- 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)
else:
cfg, create_cfg = deepcopy(input_cfg)
create_cfg.policy.type = 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)
# 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])
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)
set_pkg_seed(cfg.seed, use_cuda=cfg.policy.cuda)
policy = create_policy(cfg.policy, model=model, enable_field=['learn', 'collect', 'eval', 'command'])
# 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
)
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)
replay_buffer_recent = 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
)
# ==========
# Main loop
# ==========
# Learner's before_run hook.
learner.call_hook('before_run')
# Accumulate plenty of data at the beginning of training.
if cfg.policy.get('random_collect_size', 0) > 0:
# backup
# if cfg.policy.get('transition_with_policy_data', False):
# collector.reset_policy(policy.collect_mode)
# else:
# action_space = collector_env.action_space
# random_policy = PolicyFactory.get_random_policy(policy.collect_mode, action_space=action_space)
# collector.reset_policy(random_policy)
# collect_kwargs = commander.step()
# new_data = collector.collect(n_sample=cfg.policy.random_collect_size, policy_kwargs=collect_kwargs)
# for item in new_data:
# item['warm_up'] = True
# replay_buffer.push(new_data, cur_collector_envstep=0)
# collector.reset_policy(policy.collect_mode)
# postprocess_data_fn = lambda x: mark_warm_up(mark_not_expert(x))
random_collect(
cfg.policy,
policy,
collector,
collector_env,
commander,
replay_buffer,
postprocess_data_fn=lambda x: mark_warm_up(mark_not_expert(x)) # postprocess_data_fn
)
# warm_up
# Learn policy from collected data
for i in range(cfg.policy.learn.warm_up_update):
# 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)
replay_buffer.clear() # NOTE
# NOTE: for the case collector_env_num>1, because after the random collect phase, self._traj_buffer[env_id] may
# be not empty. Only if the condition "timestep.done or len(self._traj_buffer[env_id]) == self._traj_len" is
# satisfied, the self._traj_buffer will be clear. For our alg., the data in self._traj_buffer[env_id],
# latent_action=False, cannot be used in rl_vae phase.
collector.reset(policy.collect_mode)
count = 0
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 item in new_data:
item['warm_up'] = False
replay_buffer.push(new_data, cur_collector_envstep=collector.envstep)
replay_buffer_recent.push(copy.deepcopy(new_data), cur_collector_envstep=collector.envstep)
# rl phase
if count % cfg.policy.learn.rl_vae_update_circle in range(0, cfg.policy.learn.rl_vae_update_circle):
# Learn policy from collected data
for i in range(cfg.policy.learn.update_per_collect_rl):
# 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 not None:
for item in train_data:
item['rl_phase'] = True
item['vae_phase'] = False
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)
# vae phase
if count % cfg.policy.learn.rl_vae_update_circle in range(cfg.policy.learn.rl_vae_update_circle - 1,
cfg.policy.learn.rl_vae_update_circle):
for i in range(cfg.policy.learn.update_per_collect_vae):
# Learner will train ``update_per_collect`` times in one iteration.
# TODO(pu): different sample style
train_data_history = replay_buffer.sample(
int(learner.policy.get_attribute('batch_size') / 2), learner.train_iter
)
train_data_recent = replay_buffer_recent.sample(
int(learner.policy.get_attribute('batch_size') / 2), learner.train_iter
)
train_data = train_data_history + train_data_recent
if train_data is not None:
for item in train_data:
item['rl_phase'] = False
item['vae_phase'] = True
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)
replay_buffer_recent.clear() # NOTE
if collector.envstep >= max_env_step or learner.train_iter >= max_train_iter:
break
count += 1
# Learner's after_run hook.
learner.call_hook('after_run')
return policy
|