File size: 13,252 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 |
from collections import namedtuple
from typing import Optional, Callable, Tuple
import torch
import numpy as np
from ding.envs import BaseEnv
from ding.envs import BaseEnvManager
from ding.torch_utils import to_tensor, to_item
from ding.utils import build_logger, EasyTimer, SERIAL_EVALUATOR_REGISTRY
from ding.utils import get_world_size, get_rank, broadcast_object_list
from ding.worker.collector.base_serial_evaluator import ISerialEvaluator, VectorEvalMonitor
@SERIAL_EVALUATOR_REGISTRY.register('alphazero')
class AlphaZeroEvaluator(ISerialEvaluator):
"""
Overview:
AlphaZero Evaluator.
Interfaces:
__init__, reset, reset_policy, reset_env, close, should_eval, eval
Property:
env, policy
"""
def __init__(
self,
eval_freq: int = 1000,
n_evaluator_episode: int = 3,
stop_value: int = 1e6,
env: BaseEnv = None,
policy: namedtuple = None,
tb_logger: 'SummaryWriter' = None, # noqa
exp_name: Optional[str] = 'default_experiment',
instance_name: Optional[str] = 'evaluator',
env_config=None,
) -> None:
"""
Overview:
Init the AlphaZero evaluator according to input arguments.
Arguments:
- eval_freq (:obj:`int`): evaluation frequency in terms of training steps.
- n_evaluator_episode (:obj:`int`): the number of episodes to eval in total.
- env (:obj:`BaseEnvManager`): The env for the collection, the BaseEnvManager object or \
its derivatives are supported.
- policy (:obj:`Policy`): The policy to be collected.
- tb_logger (:obj:`SummaryWriter`): Logger, defaultly set as 'SummaryWriter' for model summary.
- exp_name (:obj:`str`): Experiment name, which is used to indicate output directory.
- instance_name (:obj:`Optional[str]`): Name of this instance.
- env_config: Config of environment
"""
self._eval_freq = eval_freq
self._exp_name = exp_name
self._instance_name = instance_name
self._end_flag = False
self._env_config = env_config
# Logger (Monitor will be initialized in policy setter)
# Only rank == 0 learner needs monitor and tb_logger, others only need text_logger to display terminal output.
if get_rank() == 0:
if tb_logger is not None:
self._logger, _ = build_logger(
'./{}/log/{}'.format(self._exp_name, self._instance_name), self._instance_name, need_tb=False
)
self._tb_logger = tb_logger
else:
self._logger, self._tb_logger = build_logger(
'./{}/log/{}'.format(self._exp_name, self._instance_name), self._instance_name
)
else:
self._logger, self._tb_logger = None, None # for close elegantly
self.reset(policy, env)
self._timer = EasyTimer()
self._default_n_episode = n_evaluator_episode
self._stop_value = stop_value
def reset_env(self, _env: Optional[BaseEnvManager] = None) -> None:
"""
Overview:
Reset evaluator's environment. In some case, we need evaluator use the same policy in different \
environments. We can use reset_env to reset the environment.
If _env is None, reset the old environment.
If _env is not None, replace the old environment in the evaluator with the \
new passed in environment and launch.
Arguments:
- env (:obj:`Optional[BaseEnvManager]`): instance of the subclass of vectorized \
env_manager(BaseEnvManager)
"""
if _env is not None:
self._env = _env
self._env.launch()
self._env_num = self._env.env_num
else:
self._env.reset()
def reset_policy(self, _policy: Optional[namedtuple] = None) -> None:
"""
Overview:
Reset evaluator's policy. In some case, we need evaluator work in this same environment but use\
different policy. We can use reset_policy to reset the policy.
If _policy is None, reset the old policy.
If _policy is not None, replace the old policy in the evaluator with the new passed in policy.
Arguments:
- policy (:obj:`Optional[namedtuple]`): the api namedtuple of eval_mode policy
"""
assert hasattr(self, '_env'), "please set env first"
if _policy is not None:
self._policy = _policy
self._policy.reset()
def reset(self, _policy: Optional[namedtuple] = None, _env: Optional[BaseEnvManager] = None) -> None:
"""
Overview:
Reset evaluator's policy and environment. Use new policy and environment to collect data.
If _env is None, reset the old environment.
If _env is not None, replace the old environment in the evaluator with the new passed in \
environment and launch.
If _policy is None, reset the old policy.
If _policy is not None, replace the old policy in the evaluator with the new passed in policy.
Arguments:
- policy (:obj:`Optional[namedtuple]`): the api namedtuple of eval_mode policy
- env (:obj:`Optional[BaseEnvManager]`): instance of the subclass of vectorized \
env_manager(BaseEnvManager)
"""
if _env is not None:
self.reset_env(_env)
if _policy is not None:
self.reset_policy(_policy)
self._max_eval_reward = float("-inf")
self._last_eval_iter = -1
self._end_flag = False
def close(self) -> None:
"""
Overview:
Close the evaluator. If end_flag is False, close the environment, flush the tb_logger\
and close the tb_logger.
"""
if self._end_flag:
return
self._end_flag = True
self._env.close()
if self._tb_logger:
self._tb_logger.flush()
self._tb_logger.close()
def __del__(self) -> None:
"""
Overview:
Execute the close command and close the evaluator. __del__ is automatically called \
to destroy the evaluator instance when the evaluator finishes its work
"""
self.close()
def should_eval(self, train_iter: int) -> bool:
"""
Overview:
Determine whether you need to start the evaluation mode, if the number of training has reached\
the maximum number of times to start the evaluator, return True
Arguments:
- train_iter (:obj:`int`): Current training iteration.
"""
if train_iter == self._last_eval_iter:
return False
if (train_iter - self._last_eval_iter) < self._eval_freq and train_iter != 0:
return False
self._last_eval_iter = train_iter
return True
def eval(
self,
save_ckpt_fn: Callable = None,
train_iter: int = -1,
envstep: int = -1,
n_episode: Optional[int] = None,
force_render: bool = False,
) -> Tuple[bool, dict]:
"""
Overview:
Evaluate policy and store the best policy based on whether it reaches the highest historical reward.
Arguments:
- save_ckpt_fn (:obj:`Callable`): Saving ckpt function, which will be triggered by getting the best reward.
- train_iter (:obj:`int`): Current training iteration.
- envstep (:obj:`int`): Current env interaction step.
- n_episode (:obj:`int`): Number of evaluation episodes.
Returns:
- stop_flag (:obj:`bool`): Whether this training program can be ended.
- return_info (:obj:`dict`): Current evaluation return information.
"""
# evaluator only work on rank0
stop_flag, return_info = False, []
if get_rank() == 0:
if n_episode is None:
n_episode = self._default_n_episode
assert n_episode is not None, "please indicate eval n_episode"
envstep_count = 0
eval_monitor = VectorEvalMonitor(self._env.env_num, n_episode)
self._env.reset()
self._policy.reset()
with self._timer:
while not eval_monitor.is_finished():
obs = self._env.ready_obs
# ==============================================================
# policy forward
# ==============================================================
policy_output = self._policy.forward(obs)
actions = {env_id: output['action'] for env_id, output in policy_output.items()}
# ==============================================================
# Interact with env.
# ==============================================================
timesteps = self._env.step(actions)
timesteps = to_tensor(timesteps, dtype=torch.float32)
for env_id, t in timesteps.items():
if t.info.get('abnormal', False):
# If there is an abnormal timestep, reset all the related variables(including this env).
self._policy.reset([env_id])
continue
if t.done:
# Env reset is done by env_manager automatically.
self._policy.reset([env_id])
reward = t.info['eval_episode_return']
saved_info = {'eval_episode_return': t.info['eval_episode_return']}
if 'episode_info' in t.info:
saved_info.update(t.info['episode_info'])
eval_monitor.update_info(env_id, saved_info)
eval_monitor.update_reward(env_id, reward)
return_info.append(t.info)
self._logger.info(
"[EVALUATOR]env {} finish episode, final reward: {}, current episode: {}".format(
env_id, eval_monitor.get_latest_reward(env_id), eval_monitor.get_current_episode()
)
)
envstep_count += 1
duration = self._timer.value
episode_return = eval_monitor.get_episode_return()
info = {
'train_iter': train_iter,
'ckpt_name': 'iteration_{}.pth.tar'.format(train_iter),
'episode_count': n_episode,
'envstep_count': envstep_count,
'avg_envstep_per_episode': envstep_count / n_episode,
'evaluate_time': duration,
'avg_envstep_per_sec': envstep_count / duration,
'avg_time_per_episode': n_episode / duration,
'reward_mean': np.mean(episode_return),
'reward_std': np.std(episode_return),
'reward_max': np.max(episode_return),
'reward_min': np.min(episode_return),
# 'each_reward': episode_return,
}
episode_info = eval_monitor.get_episode_info()
if episode_info is not None:
info.update(episode_info)
self._logger.info(self._logger.get_tabulate_vars_hor(info))
# self._logger.info(self._logger.get_tabulate_vars(info))
for k, v in info.items():
if k in ['train_iter', 'ckpt_name', 'each_reward']:
continue
if not np.isscalar(v):
continue
self._tb_logger.add_scalar('{}_iter/'.format(self._instance_name) + k, v, train_iter)
self._tb_logger.add_scalar('{}_step/'.format(self._instance_name) + k, v, envstep)
eval_reward = np.mean(episode_return)
if eval_reward > self._max_eval_reward:
if save_ckpt_fn:
save_ckpt_fn('ckpt_best.pth.tar')
self._max_eval_reward = eval_reward
stop_flag = eval_reward >= self._stop_value and train_iter > 0
if stop_flag:
self._logger.info(
"[LightZero serial pipeline] " +
"Current eval_reward: {} is greater than stop_value: {}".format(eval_reward, self._stop_value) +
", so your AlphaZero agent is converged, you can refer to " +
"'log/evaluator/evaluator_logger.txt' for details."
)
if get_world_size() > 1:
objects = [stop_flag, episode_info]
broadcast_object_list(objects, src=0)
stop_flag, episode_info = objects
episode_info = to_item(episode_info)
return stop_flag, episode_info
|