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dataset.py
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dataset.py
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import torch
import numpy as np
from typing import List, Tuple
import os
class ReplayBuffer:
def __init__(self,
state_dim: int,
action_dim: int,
buffer_size: int = 1000000) -> None:
self.state_dim = state_dim
self.action_dim = action_dim
self.buffer_size = buffer_size
self.pointer = 0
self.size = 0
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
self.device = device
self.states = torch.zeros((buffer_size, state_dim), dtype=torch.float32, device=device)
self.actions = torch.zeros((buffer_size, action_dim), dtype=torch.float32, device=device)
self.rewards = torch.zeros((buffer_size, 1), dtype=torch.float32, device=device)
self.next_states = torch.zeros((buffer_size, state_dim), dtype=torch.float32, device=device)
self.dones = torch.zeros((buffer_size, 1), dtype=torch.float32, device=device)
# i/o order: state, action, reward, next_state, done
def from_json(self, json_file: str):
import json
if not json_file.endswith('.json'):
json_file = json_file + '.json'
json_file = os.path.join("json_datasets", json_file)
output = dict()
with open(json_file) as f:
dataset = json.load(f)
for k, v in dataset.items():
v = np.array(v)
if k != "terminals":
v = v.astype(np.float32)
output[k] = v
self.from_d4rl(output)
def get_moments(self) -> Tuple[Tuple[torch.Tensor, torch.Tensor], Tuple[torch.Tensor, torch.Tensor]]:
state_mean, state_std = self.states.mean(dim=0), self.states.std(dim=0)
action_mean, action_std = self.actions.mean(dim=0), self.actions.std(dim=0)
return (state_mean, state_std), (action_mean, action_std)
@staticmethod
def to_tensor(data: np.ndarray, device=None) -> torch.Tensor:
if device is None:
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
return torch.tensor(data, dtype=torch.float32, device=device)
def sample(self, batch_size: int):
indexes = np.random.randint(0, self.size, size=batch_size)
return (
self.states[indexes],
self.actions[indexes],
self.rewards[indexes],
self.next_states[indexes],
self.dones[indexes]
)
def from_d4rl(self, dataset):
if self.size:
print("Warning: loading data into non-empty buffer")
n_transitions = dataset["observations"].shape[0]
if n_transitions < self.buffer_size:
self.states[:n_transitions] = self.to_tensor(dataset["observations"][-n_transitions:], self.device)
self.actions[:n_transitions] = self.to_tensor(dataset["actions"][-n_transitions:], self.device)
self.next_states[:n_transitions] = self.to_tensor(dataset["next_observations"][-n_transitions:], self.device)
self.rewards[:n_transitions] = self.to_tensor(dataset["rewards"][-n_transitions:].reshape(-1, 1), self.device)
self.dones[:n_transitions] = self.to_tensor(dataset["terminals"][-n_transitions:].reshape(-1, 1), self.device)
else:
self.buffer_size = n_transitions
self.states = self.to_tensor(dataset["observations"][-n_transitions:], self.device)
self.actions = self.to_tensor(dataset["actions"][-n_transitions:])
self.next_states = self.to_tensor(dataset["next_observations"][-n_transitions:], self.device)
self.rewards = self.to_tensor(dataset["rewards"][-n_transitions:].reshape(-1, 1), self.device)
self.dones = self.to_tensor(dataset["terminals"][-n_transitions:].reshape(-1, 1), self.device)
self.size = n_transitions
self.pointer = n_transitions % self.buffer_size
def from_d4rl_finetune(self, dataset):
raise NotImplementedError()
def normalize_states(self, eps=1e-3):
mean = self.states.mean(0, keepdim=True)
std = self.states.std(0, keepdim=True) + eps
self.states = (self.states - mean) / std
self.next_states = (self.next_states - mean) / std
return mean, std
def clip(self, eps=1e-5):
self.actions = torch.clip(self.actions, - 1 + eps, 1 - eps)
def add_transition(self,
state: torch.Tensor,
action: torch.Tensor,
reward: torch.Tensor,
next_state: torch.Tensor,
done: torch.Tensor):
if not isinstance(state, torch.Tensor):
state = self.to_tensor(state, self.device)
action = self.to_tensor(action, self.device)
reward = self.to_tensor(reward, self.device)
next_state = self.to_tensor(next_state, self.device)
done = self.to_tensor(done, self.device)
self.states[self.pointer] = state
self.actions[self.pointer] = action
self.rewards[self.pointer] = reward
self.next_states[self.pointer] = next_state
self.dones[self.pointer] = done
self.pointer = (self.pointer + 1) % self.buffer_size
self.size = min(self.size + 1, self.buffer_size)
def add_batch(self,
states: List[torch.Tensor],
actions: List[torch.Tensor],
rewards: List[torch.Tensor],
next_states: List[torch.Tensor],
dones: List[torch.Tensor]):
for state, action, reward, next_state, done in zip(states, actions, rewards, next_states, dones):
self.add_transition(state, action, reward, next_state, done)
@staticmethod
def dataset_stats(dataset):
episode_returns = []
returns = 0
episode_length = 0
for reward, done in zip(dataset["rewards"], dataset["terminals"]):
if done:
episode_returns.append(returns)
returns = 0
episode_length = 0
else:
episode_length += 1
returns += reward
if episode_length == 1000:
episode_returns.append(returns)
returns = 0
episode_length = 0
episode_returns = np.array(episode_returns)
return episode_returns.mean(), episode_returns.std()
def train_val_split(replay_buffer: ReplayBuffer, val_size: float) -> Tuple[ReplayBuffer, ReplayBuffer]:
data_size = replay_buffer.size
val_size = int(data_size * val_size)
permutation = torch.randperm(data_size)
train_rb = ReplayBuffer(replay_buffer.state_dim, replay_buffer.action_dim)
val_rb = ReplayBuffer(replay_buffer.state_dim, replay_buffer.action_dim)
train_rb.add_batch(
replay_buffer.states[permutation[val_size:]],
replay_buffer.actions[permutation[val_size:]],
replay_buffer.rewards[permutation[val_size:]],
replay_buffer.next_states[permutation[val_size:]],
replay_buffer.dones[permutation[val_size:]]
)
val_rb.add_batch(
replay_buffer.states[permutation[:val_size]],
replay_buffer.actions[permutation[:val_size]],
replay_buffer.rewards[permutation[:val_size]],
replay_buffer.next_states[permutation[:val_size]],
replay_buffer.dones[permutation[:val_size]]
)
return train_rb, val_rb