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executable file
·284 lines (235 loc) · 10.8 KB
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from math import gamma
from random import randint
from typing import List, Dict
import pickle
import torch
import numpy as np
from tqdm import tqdm
import logging
from easydict import EasyDict
from torch.utils.data import Dataset
from ding.utils import DATASET_REGISTRY, import_module
@DATASET_REGISTRY.register('naive')
class NaiveRLDataset(Dataset):
def __init__(self, cfg) -> None:
assert type(cfg) in [str, EasyDict], "invalid cfg type: {}".format(type(cfg))
if isinstance(cfg, EasyDict):
self._data_path = cfg.policy.collect.data_path
elif isinstance(cfg, str):
self._data_path = cfg
with open(self._data_path, 'rb') as f:
self._data: List[Dict[str, torch.Tensor]] = pickle.load(f)
def __len__(self) -> int:
return len(self._data)
def __getitem__(self, idx: int) -> Dict[str, torch.Tensor]:
return self._data[idx]
@DATASET_REGISTRY.register('d4rl')
class D4RLDataset(Dataset):
def __init__(self, cfg: dict) -> None:
import gym
import logging
from time import sleep,time
import random
import d4rl
# for i in range(100):
# try:
# import d4rl # register d4rl enviroments with open ai gym
# except Exception as e:
# random.seed(time())
# sleep(random.randint(1,60))
# print(e)
# try:
# import d4rl # register d4rl enviroments with open ai gym
# except ImportError:
# logging.warning("not found d4rl env, please install it, refer to https://github.com/rail-berkeley/d4rl")
# Init parameters
data_path = cfg.policy.collect.get('data_path', None)
env_id = cfg.env.env_id
# Create the environment
if data_path:
d4rl.set_dataset_path(data_path)
env = gym.make(env_id)
if cfg.env.get('with_q_value',None):
dataset,self._q_min,self._q_max = d4rl.qlearning_dataset_with_q(env, gamma=cfg.policy.learn.discount_factor, norm=cfg.policy.learn.get('normalize_q',None))
else:
dataset = d4rl.qlearning_dataset(env)
self._data = []
self._dataset = dataset
if cfg.policy.learn.get('normalize_states', None):
self._normalize_states(dataset)
if cfg.policy.learn.get('local_q_target', None):
dataset = self._state_action_local_target_q(dataset,num_clusters=cfg.policy.learn.local_q_target_num_clusters,
quantile=cfg.policy.learn.local_q_target_quantile)
self._next_action = cfg.policy.collect.get('next_action', None)
self._load_d4rl(cfg, dataset)
def __len__(self) -> int:
return len(self._data)
def __getitem__(self, idx: int) -> Dict[str, torch.Tensor]:
return self._data[idx]
def _load_d4rl(self,cfg: dict, dataset: Dict[str, np.ndarray]) -> None:
for i in range(len(dataset['observations'])):
trans_data = {}
trans_data['obs'] = torch.from_numpy(dataset['observations'][i])
trans_data['next_obs'] = torch.from_numpy(dataset['next_observations'][i])
trans_data['action'] = torch.from_numpy(dataset['actions'][i])
trans_data['reward'] = torch.tensor(dataset['rewards'][i])
trans_data['done'] = dataset['terminals'][i]
if cfg.env.get('with_q_value',None):
trans_data['q_value'] = torch.tensor(dataset['q_values'][i])
if cfg.policy.learn.get('local_q_target', None):
trans_data['q_target'] = torch.tensor(dataset['q_target'][i])
if self._next_action:
trans_data['next_action'] = torch.from_numpy(dataset['actions'][(i+1)%len(dataset['observations'])])
trans_data['collect_iter'] = 0
self._data.append(trans_data)
def _normalize_states(self, dataset, eps=1e-5):
self._mean = dataset['observations'].mean(0, keepdims=True)
self._std = dataset['observations'].std(0, keepdims=True) + eps
dataset['observations'] = (dataset['observations'] - self._mean) / self._std
dataset['next_observations'] = (dataset['next_observations'] - self._mean) / self._std
self._mean = torch.from_numpy(self._mean)
self._std = torch.from_numpy(self._std)
def _state_action_local_target_q(self, dataset, num_clusters=1000, quantile=0.6):
# init
state = dataset['observations']
action = dataset['actions']
q_value = dataset['q_values']
data = np.concatenate([state, action], axis=1)
# cluster
import sklearn
from sklearn.cluster import MiniBatchKMeans
kmeans = MiniBatchKMeans(n_clusters=num_clusters, batch_size=10240, random_state=0).fit(data)
q_target_list = [q_value[kmeans.labels_==i] for i in range(num_clusters)]
# sort
[q.sort() for q in q_target_list]
q_target = [q[int(quantile*len(q))] for q in q_target_list]
# assign
dataset['q_target'] = np.array(q_target)[kmeans.labels_]
return dataset
@property
def mean(self):
return self._mean
@property
def std(self):
return self._std
@property
def data(self):
return self._data
@property
def raw_dataset(self):
return self._dataset
@DATASET_REGISTRY.register('d4rl_space')
class D4RLSpaceDataset(Dataset):
def __init__(self, cfg: dict) -> None:
import gym
import random
import d4rl
# Init parameters
data_path = cfg.policy.collect.get('data_path', None)
env_id = cfg.env.env_id
self.k = cfg.env.k
# Create the environment
if data_path:
d4rl.set_dataset_path(data_path)
env = gym.make(env_id)
dataset = env.get_dataset()
timeouts = dataset['timeouts'][:-1]
dataset = d4rl.qlearning_dataset(env, dataset=dataset, terminate_on_end=True)
dataset['timeouts'] = timeouts
self._load_d4rl(cfg, dataset)
def __len__(self) -> int:
return len(self._data)
def __getitem__(self, idx: int) -> Dict[str, torch.Tensor]:
return self._data[idx]
def _load_d4rl(self,cfg: dict, dataset: Dict[str, np.ndarray]) -> None:
obs = torch.from_numpy(dataset['observations'])
next_obs = torch.from_numpy(dataset['next_observations'])
action = torch.from_numpy(dataset['actions'])
reward = torch.from_numpy(dataset['rewards'])
done = torch.from_numpy(dataset['terminals']) & ~torch.from_numpy(dataset['timeouts'])
self._mean = obs.mean(0, keepdim=True)
self._std = obs.std(0, keepdim=True)
self._obs_shape = obs.shape[-1]
self._action_shape = action.shape[-1]
action_space, dist_space = self._generate_action_space(obs, action, obs)
next_action_space, next_dist_space = self._generate_action_space(next_obs, action, obs)
self._data = [{'obs': obs[i], 'next_obs': next_obs[i], 'action': action[i], 'reward': reward[i], 'done': done[i], 'action_space': action_space[i], 'next_action_space': next_action_space[i], 'dist_space': dist_space[i], 'next_dist_space': next_dist_space[i]} for i in range(len(obs))]
def _generate_action_space(self, obs, action, obs_set):
import faiss
obs = ((obs - self._mean) / self._std).numpy()
obs_set = ((obs_set - self._mean) / self._std).numpy()
action_space = []
dist_space = []
res = faiss.StandardGpuResources()
index = faiss.IndexFlatL2(self._obs_shape)
index = faiss.index_cpu_to_gpu(res, 0, index)
index.add(obs_set)
batch_size = 1024
for i in tqdm(range(0, len(obs), batch_size)):
dist, k_id = index.search(obs[i: i + batch_size], self.k)
action_space.append(action[torch.from_numpy(k_id).long()])
dist_space.append(torch.from_numpy(dist / np.sqrt(self._obs_shape)))
return torch.cat(action_space, dim=0), torch.cat(dist_space, dim=0)
@property
def mean(self):
return self._mean
@property
def std(self):
return self._std
@property
def data(self):
return self._data
@DATASET_REGISTRY.register('hdf5')
class HDF5Dataset(Dataset):
def __init__(self, cfg: dict) -> None:
try:
import h5py
except ImportError:
logging.warning("not found h5py package, please install it trough 'pip install h5py' ")
data_path = cfg.policy.collect.get('data_path', None)
data = h5py.File(data_path, 'r')
self._load_data(data)
if cfg.policy.collect.get('normalize_states', None):
self._normalize_states()
def __len__(self) -> int:
return len(self._data['obs'])
def __getitem__(self, idx: int) -> Dict[str, torch.Tensor]:
return {k: self._data[k][idx] for k in self._data.keys()}
def _load_data(self, dataset: Dict[str, np.ndarray]) -> None:
self._data = {}
for k in dataset.keys():
logging.info(f'Load {k} data.')
self._data[k] = dataset[k][:]
def _normalize_states(self, eps=1e-3):
self._mean = self._data['obs'].mean(0, keepdims=True)
self._std = self._data['obs'].std(0, keepdims=True) + eps
self._data['obs'] = (self._data['obs'] - self._mean) / self._std
self._data['next_obs'] = (self._data['next_obs'] - self._mean) / self._std
@property
def mean(self):
return self._mean
@property
def std(self):
return self._std
def hdf5_save(exp_data, expert_data_path):
try:
import h5py
except ImportError:
logging.warning("not found h5py package, please install it trough 'pip install h5py' ")
import numpy as np
dataset = dataset = h5py.File('%s_demos.hdf5' % expert_data_path.replace('.pkl', ''), 'w')
dataset.create_dataset('obs', data=np.array([d['obs'].numpy() for d in exp_data]), compression='gzip')
dataset.create_dataset('action', data=np.array([d['action'].numpy() for d in exp_data]), compression='gzip')
dataset.create_dataset('reward', data=np.array([d['reward'].numpy() for d in exp_data]), compression='gzip')
dataset.create_dataset('done', data=np.array([d['done'] for d in exp_data]), compression='gzip')
dataset.create_dataset('collect_iter', data=np.array([d['collect_iter'] for d in exp_data]), compression='gzip')
dataset.create_dataset('next_obs', data=np.array([d['next_obs'].numpy() for d in exp_data]), compression='gzip')
def naive_save(exp_data, expert_data_path):
with open(expert_data_path, 'wb') as f:
pickle.dump(exp_data, f)
def offline_data_save_type(exp_data, expert_data_path, data_type='naive'):
globals()[data_type + '_save'](exp_data, expert_data_path)
def create_dataset(cfg, **kwargs) -> Dataset:
cfg = EasyDict(cfg)
import_module(cfg.get('import_names', []))
return DATASET_REGISTRY.build(cfg.policy.collect.data_type, cfg=cfg, **kwargs)