-
Notifications
You must be signed in to change notification settings - Fork 0
/
dataset.py
141 lines (109 loc) · 5.57 KB
/
dataset.py
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
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 = "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
self.normalize_states()
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 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)