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Source code for ding.world_model.mbpo

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


[docs]@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