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from typing import Tuple, TypeVar
import gym
import numpy as np
from numpy.typing import NDArray
from rl_algo_impls.wrappers.vectorable_wrapper import (
VecotarableWrapper,
single_observation_space,
)
RunningMeanStdSelf = TypeVar("RunningMeanStdSelf", bound="RunningMeanStd")
class RunningMeanStd:
def __init__(self, episilon: float = 1e-4, shape: Tuple[int, ...] = ()) -> None:
self.mean = np.zeros(shape, np.float64)
self.var = np.ones(shape, np.float64)
self.count = episilon
def update(self, x: NDArray) -> None:
batch_mean = np.mean(x, axis=0)
batch_var = np.var(x, axis=0)
batch_count = x.shape[0]
delta = batch_mean - self.mean
total_count = self.count + batch_count
self.mean += delta * batch_count / total_count
m_a = self.var * self.count
m_b = batch_var * batch_count
M2 = m_a + m_b + np.square(delta) * self.count * batch_count / total_count
self.var = M2 / total_count
self.count = total_count
def save(self, path: str) -> None:
np.savez_compressed(
path,
mean=self.mean,
var=self.var,
count=self.count,
)
def load(self, path: str) -> None:
data = np.load(path)
self.mean = data["mean"]
self.var = data["var"]
self.count = data["count"]
def load_from(self: RunningMeanStdSelf, existing: RunningMeanStdSelf) -> None:
self.mean = np.copy(existing.mean)
self.var = np.copy(existing.var)
self.count = np.copy(existing.count)
NormalizeObservationSelf = TypeVar(
"NormalizeObservationSelf", bound="NormalizeObservation"
)
class NormalizeObservation(VecotarableWrapper):
def __init__(
self,
env: gym.Env,
training: bool = True,
epsilon: float = 1e-8,
clip: float = 10.0,
) -> None:
super().__init__(env)
self.rms = RunningMeanStd(shape=single_observation_space(env).shape)
self.training = training
self.epsilon = epsilon
self.clip = clip
def step(self, action):
obs, reward, done, info = self.env.step(action)
return self.normalize(obs), reward, done, info
def reset(self, **kwargs):
obs = self.env.reset(**kwargs)
return self.normalize(obs)
def normalize(self, obs: NDArray) -> NDArray:
obs_array = np.array([obs]) if not self.is_vector_env else obs
if self.training:
self.rms.update(obs_array)
normalized = np.clip(
(obs_array - self.rms.mean) / np.sqrt(self.rms.var + self.epsilon),
-self.clip,
self.clip,
)
return normalized[0] if not self.is_vector_env else normalized
def save(self, path: str) -> None:
self.rms.save(path)
def load(self, path: str) -> None:
self.rms.load(path)
def load_from(
self: NormalizeObservationSelf, existing: NormalizeObservationSelf
) -> None:
self.rms.load_from(existing.rms)
NormalizeRewardSelf = TypeVar("NormalizeRewardSelf", bound="NormalizeReward")
class NormalizeReward(VecotarableWrapper):
def __init__(
self,
env: gym.Env,
training: bool = True,
gamma: float = 0.99,
epsilon: float = 1e-8,
clip: float = 10.0,
) -> None:
super().__init__(env)
self.rms = RunningMeanStd(shape=())
self.training = training
self.gamma = gamma
self.epsilon = epsilon
self.clip = clip
self.returns = np.zeros(self.num_envs)
def step(self, action):
obs, reward, done, info = self.env.step(action)
if not self.is_vector_env:
reward = np.array([reward])
reward = self.normalize(reward)
if not self.is_vector_env:
reward = reward[0]
dones = done if self.is_vector_env else np.array([done])
self.returns[dones] = 0
return obs, reward, done, info
def reset(self, **kwargs):
self.returns = np.zeros(self.num_envs)
return self.env.reset(**kwargs)
def normalize(self, rewards):
if self.training:
self.returns = self.returns * self.gamma + rewards
self.rms.update(self.returns)
return np.clip(
rewards / np.sqrt(self.rms.var + self.epsilon), -self.clip, self.clip
)
def save(self, path: str) -> None:
self.rms.save(path)
def load(self, path: str) -> None:
self.rms.load(path)
def load_from(self: NormalizeRewardSelf, existing: NormalizeRewardSelf) -> None:
self.rms.load_from(existing.rms)
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