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dataset_utils.py
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dataset_utils.py
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import collections
from typing import Optional
import d4rl
import gym
import numpy as np
from tqdm import tqdm
Batch = collections.namedtuple(
'Batch',
['observations', 'actions', 'rewards', 'masks', 'next_observations'])
def split_into_trajectories(observations, actions, rewards, masks, dones_float,
next_observations):
trajs = [[]]
for i in tqdm(range(len(observations))):
trajs[-1].append((observations[i], actions[i], rewards[i], masks[i],
dones_float[i], next_observations[i]))
if dones_float[i] == 1.0 and i + 1 < len(observations):
trajs.append([])
return trajs
def merge_trajectories(trajs):
observations = []
actions = []
rewards = []
masks = []
dones_float = []
next_observations = []
for traj in trajs:
for (obs, act, rew, mask, done, next_obs) in traj:
observations.append(obs)
actions.append(act)
rewards.append(rew)
masks.append(mask)
dones_float.append(done)
next_observations.append(next_obs)
return np.stack(observations), np.stack(actions), np.stack(
rewards), np.stack(masks), np.stack(dones_float), np.stack(
next_observations)
class Dataset(object):
def __init__(self, observations: np.ndarray, actions: np.ndarray,
rewards: np.ndarray, masks: np.ndarray,
dones_float: np.ndarray, next_observations: np.ndarray,
size: int):
self.observations = observations
self.actions = actions
self.rewards = rewards
self.masks = masks
self.dones_float = dones_float
self.next_observations = next_observations
self.size = size
def sample(self, batch_size: int) -> Batch:
indx = np.random.randint(self.size, size=batch_size)
return Batch(observations=self.observations[indx],
actions=self.actions[indx],
rewards=self.rewards[indx],
masks=self.masks[indx],
next_observations=self.next_observations[indx])
class D4RLDataset(Dataset):
def __init__(self,
env: gym.Env,
clip_to_eps: bool = True,
eps: float = 1e-5):
dataset = d4rl.qlearning_dataset(env)
if clip_to_eps:
lim = 1 - eps
dataset['actions'] = np.clip(dataset['actions'], -lim, lim)
dones_float = np.zeros_like(dataset['rewards'])
for i in range(len(dones_float) - 1):
if np.linalg.norm(dataset['observations'][i + 1] -
dataset['next_observations'][i]
) > 1e-6 or dataset['terminals'][i] == 1.0:
dones_float[i] = 1
else:
dones_float[i] = 0
dones_float[-1] = 1
super().__init__(dataset['observations'].astype(np.float32),
actions=dataset['actions'].astype(np.float32),
rewards=dataset['rewards'].astype(np.float32),
masks=1.0 - dataset['terminals'].astype(np.float32),
dones_float=dones_float.astype(np.float32),
next_observations=dataset['next_observations'].astype(
np.float32),
size=len(dataset['observations']))
class ReplayBuffer(Dataset):
def __init__(self, observation_space: gym.spaces.Box, action_dim: int,
capacity: int):
observations = np.empty((capacity, *observation_space.shape),
dtype=observation_space.dtype)
actions = np.empty((capacity, action_dim), dtype=np.float32)
rewards = np.empty((capacity, ), dtype=np.float32)
masks = np.empty((capacity, ), dtype=np.float32)
dones_float = np.empty((capacity, ), dtype=np.float32)
next_observations = np.empty((capacity, *observation_space.shape),
dtype=observation_space.dtype)
super().__init__(observations=observations,
actions=actions,
rewards=rewards,
masks=masks,
dones_float=dones_float,
next_observations=next_observations,
size=0)
self.size = 0
self.insert_index = 0
self.capacity = capacity
def initialize_with_dataset(self, dataset: Dataset,
num_samples: Optional[int]):
assert self.insert_index == 0, 'Can insert a batch online in an empty replay buffer.'
dataset_size = len(dataset.observations)
if num_samples is None:
num_samples = dataset_size
else:
num_samples = min(dataset_size, num_samples)
assert self.capacity >= num_samples, 'Dataset cannot be larger than the replay buffer capacity.'
if num_samples < dataset_size:
perm = np.random.permutation(dataset_size)
indices = perm[:num_samples]
else:
indices = np.arange(num_samples)
self.observations[:num_samples] = dataset.observations[indices]
self.actions[:num_samples] = dataset.actions[indices]
self.rewards[:num_samples] = dataset.rewards[indices]
self.masks[:num_samples] = dataset.masks[indices]
self.dones_float[:num_samples] = dataset.dones_float[indices]
self.next_observations[:num_samples] = dataset.next_observations[
indices]
self.insert_index = num_samples
self.size = num_samples
def insert(self, observation: np.ndarray, action: np.ndarray,
reward: float, mask: float, done_float: float,
next_observation: np.ndarray):
self.observations[self.insert_index] = observation
self.actions[self.insert_index] = action
self.rewards[self.insert_index] = reward
self.masks[self.insert_index] = mask
self.dones_float[self.insert_index] = done_float
self.next_observations[self.insert_index] = next_observation
self.insert_index = (self.insert_index + 1) % self.capacity
self.size = min(self.size + 1, self.capacity)