File size: 11,421 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 |
import itertools
import numpy as np
import copy
import torch
from torch import nn
from ding.utils import WORLD_MODEL_REGISTRY
from ding.utils.data import default_collate
from ding.world_model.base_world_model import HybridWorldModel
from ding.world_model.model.ensemble import EnsembleModel, StandardScaler
from ding.torch_utils import fold_batch, unfold_batch, unsqueeze_repeat
@WORLD_MODEL_REGISTRY.register('mbpo')
class MBPOWorldModel(HybridWorldModel, nn.Module):
config = dict(
model=dict(
ensemble_size=7,
elite_size=5,
state_size=None,
action_size=None,
reward_size=1,
hidden_size=200,
use_decay=False,
batch_size=256,
holdout_ratio=0.2,
max_epochs_since_update=5,
deterministic_rollout=True,
),
)
def __init__(self, cfg, env, tb_logger):
HybridWorldModel.__init__(self, cfg, env, tb_logger)
nn.Module.__init__(self)
cfg = cfg.model
self.ensemble_size = cfg.ensemble_size
self.elite_size = cfg.elite_size
self.state_size = cfg.state_size
self.action_size = cfg.action_size
self.reward_size = cfg.reward_size
self.hidden_size = cfg.hidden_size
self.use_decay = cfg.use_decay
self.batch_size = cfg.batch_size
self.holdout_ratio = cfg.holdout_ratio
self.max_epochs_since_update = cfg.max_epochs_since_update
self.deterministic_rollout = cfg.deterministic_rollout
self.ensemble_model = EnsembleModel(
self.state_size,
self.action_size,
self.reward_size,
self.ensemble_size,
self.hidden_size,
use_decay=self.use_decay
)
self.scaler = StandardScaler(self.state_size + self.action_size)
if self._cuda:
self.cuda()
self.ensemble_mse_losses = []
self.model_variances = []
self.elite_model_idxes = []
def step(self, obs, act, batch_size=8192, keep_ensemble=False):
if len(act.shape) == 1:
act = act.unsqueeze(1)
if self._cuda:
obs = obs.cuda()
act = act.cuda()
inputs = torch.cat([obs, act], dim=-1)
if keep_ensemble:
inputs, dim = fold_batch(inputs, 1)
inputs = self.scaler.transform(inputs)
inputs = unfold_batch(inputs, dim)
else:
inputs = self.scaler.transform(inputs)
# predict
ensemble_mean, ensemble_var = [], []
batch_dim = 0 if len(inputs.shape) == 2 else 1
for i in range(0, inputs.shape[batch_dim], batch_size):
if keep_ensemble:
# inputs: [E, B, D]
input = inputs[:, i:i + batch_size]
else:
# input: [B, D]
input = unsqueeze_repeat(inputs[i:i + batch_size], self.ensemble_size)
b_mean, b_var = self.ensemble_model(input, ret_log_var=False)
ensemble_mean.append(b_mean)
ensemble_var.append(b_var)
ensemble_mean = torch.cat(ensemble_mean, 1)
ensemble_var = torch.cat(ensemble_var, 1)
if keep_ensemble:
ensemble_mean[:, :, 1:] += obs
else:
ensemble_mean[:, :, 1:] += obs.unsqueeze(0)
ensemble_std = ensemble_var.sqrt()
# sample from the predicted distribution
if self.deterministic_rollout:
ensemble_sample = ensemble_mean
else:
ensemble_sample = ensemble_mean + torch.randn_like(ensemble_mean).to(ensemble_mean) * ensemble_std
if keep_ensemble:
# [E, B, D]
rewards, next_obs = ensemble_sample[:, :, 0], ensemble_sample[:, :, 1:]
next_obs_flatten, dim = fold_batch(next_obs)
done = unfold_batch(self.env.termination_fn(next_obs_flatten), dim)
return rewards, next_obs, done
# sample from ensemble
model_idxes = torch.from_numpy(np.random.choice(self.elite_model_idxes, size=len(obs))).to(inputs.device)
batch_idxes = torch.arange(len(obs)).to(inputs.device)
sample = ensemble_sample[model_idxes, batch_idxes]
rewards, next_obs = sample[:, 0], sample[:, 1:]
return rewards, next_obs, self.env.termination_fn(next_obs)
def eval(self, env_buffer, envstep, train_iter):
data = env_buffer.sample(self.eval_freq, train_iter)
data = default_collate(data)
data['done'] = data['done'].float()
data['weight'] = data.get('weight', None)
obs = data['obs']
action = data['action']
reward = data['reward']
next_obs = data['next_obs']
if len(reward.shape) == 1:
reward = reward.unsqueeze(1)
if len(action.shape) == 1:
action = action.unsqueeze(1)
# build eval samples
inputs = torch.cat([obs, action], dim=1)
labels = torch.cat([reward, next_obs - obs], dim=1)
if self._cuda:
inputs = inputs.cuda()
labels = labels.cuda()
# normalize
inputs = self.scaler.transform(inputs)
# repeat for ensemble
inputs = unsqueeze_repeat(inputs, self.ensemble_size)
labels = unsqueeze_repeat(labels, self.ensemble_size)
# eval
with torch.no_grad():
mean, logvar = self.ensemble_model(inputs, ret_log_var=True)
loss, mse_loss = self.ensemble_model.loss(mean, logvar, labels)
ensemble_mse_loss = torch.pow(mean.mean(0) - labels[0], 2)
model_variance = mean.var(0)
self.tb_logger.add_scalar('env_model_step/eval_mse_loss', mse_loss.mean().item(), envstep)
self.tb_logger.add_scalar('env_model_step/eval_ensemble_mse_loss', ensemble_mse_loss.mean().item(), envstep)
self.tb_logger.add_scalar('env_model_step/eval_model_variances', model_variance.mean().item(), envstep)
self.last_eval_step = envstep
def train(self, env_buffer, envstep, train_iter):
data = env_buffer.sample(env_buffer.count(), train_iter)
data = default_collate(data)
data['done'] = data['done'].float()
data['weight'] = data.get('weight', None)
obs = data['obs']
action = data['action']
reward = data['reward']
next_obs = data['next_obs']
if len(reward.shape) == 1:
reward = reward.unsqueeze(1)
if len(action.shape) == 1:
action = action.unsqueeze(1)
# build train samples
inputs = torch.cat([obs, action], dim=1)
labels = torch.cat([reward, next_obs - obs], dim=1)
if self._cuda:
inputs = inputs.cuda()
labels = labels.cuda()
# train
logvar = self._train(inputs, labels)
self.last_train_step = envstep
# log
if self.tb_logger is not None:
for k, v in logvar.items():
self.tb_logger.add_scalar('env_model_step/' + k, v, envstep)
def _train(self, inputs, labels):
#split
num_holdout = int(inputs.shape[0] * self.holdout_ratio)
train_inputs, train_labels = inputs[num_holdout:], labels[num_holdout:]
holdout_inputs, holdout_labels = inputs[:num_holdout], labels[:num_holdout]
#normalize
self.scaler.fit(train_inputs)
train_inputs = self.scaler.transform(train_inputs)
holdout_inputs = self.scaler.transform(holdout_inputs)
#repeat for ensemble
holdout_inputs = unsqueeze_repeat(holdout_inputs, self.ensemble_size)
holdout_labels = unsqueeze_repeat(holdout_labels, self.ensemble_size)
self._epochs_since_update = 0
self._snapshots = {i: (-1, 1e10) for i in range(self.ensemble_size)}
self._save_states()
for epoch in itertools.count():
train_idx = torch.stack([torch.randperm(train_inputs.shape[0])
for _ in range(self.ensemble_size)]).to(train_inputs.device)
self.mse_loss = []
for start_pos in range(0, train_inputs.shape[0], self.batch_size):
idx = train_idx[:, start_pos:start_pos + self.batch_size]
train_input = train_inputs[idx]
train_label = train_labels[idx]
mean, logvar = self.ensemble_model(train_input, ret_log_var=True)
loss, mse_loss = self.ensemble_model.loss(mean, logvar, train_label)
self.ensemble_model.train(loss)
self.mse_loss.append(mse_loss.mean().item())
self.mse_loss = sum(self.mse_loss) / len(self.mse_loss)
with torch.no_grad():
holdout_mean, holdout_logvar = self.ensemble_model(holdout_inputs, ret_log_var=True)
_, holdout_mse_loss = self.ensemble_model.loss(holdout_mean, holdout_logvar, holdout_labels)
self.curr_holdout_mse_loss = holdout_mse_loss.mean().item()
break_train = self._save_best(epoch, holdout_mse_loss)
if break_train:
break
self._load_states()
with torch.no_grad():
holdout_mean, holdout_logvar = self.ensemble_model(holdout_inputs, ret_log_var=True)
_, holdout_mse_loss = self.ensemble_model.loss(holdout_mean, holdout_logvar, holdout_labels)
sorted_loss, sorted_loss_idx = holdout_mse_loss.sort()
sorted_loss = sorted_loss.detach().cpu().numpy().tolist()
sorted_loss_idx = sorted_loss_idx.detach().cpu().numpy().tolist()
self.elite_model_idxes = sorted_loss_idx[:self.elite_size]
self.top_holdout_mse_loss = sorted_loss[0]
self.middle_holdout_mse_loss = sorted_loss[self.ensemble_size // 2]
self.bottom_holdout_mse_loss = sorted_loss[-1]
self.best_holdout_mse_loss = holdout_mse_loss.mean().item()
return {
'mse_loss': self.mse_loss,
'curr_holdout_mse_loss': self.curr_holdout_mse_loss,
'best_holdout_mse_loss': self.best_holdout_mse_loss,
'top_holdout_mse_loss': self.top_holdout_mse_loss,
'middle_holdout_mse_loss': self.middle_holdout_mse_loss,
'bottom_holdout_mse_loss': self.bottom_holdout_mse_loss,
}
def _save_states(self, ):
self._states = copy.deepcopy(self.state_dict())
def _save_state(self, id):
state_dict = self.state_dict()
for k, v in state_dict.items():
if 'weight' in k or 'bias' in k:
self._states[k].data[id] = copy.deepcopy(v.data[id])
def _load_states(self):
self.load_state_dict(self._states)
def _save_best(self, epoch, holdout_losses):
updated = False
for i in range(len(holdout_losses)):
current = holdout_losses[i]
_, best = self._snapshots[i]
improvement = (best - current) / best
if improvement > 0.01:
self._snapshots[i] = (epoch, current)
self._save_state(i)
# self._save_state(i)
updated = True
# improvement = (best - current) / best
if updated:
self._epochs_since_update = 0
else:
self._epochs_since_update += 1
return self._epochs_since_update > self.max_epochs_since_update
|