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#!/usr/bin/env python3
import numpy as np
from .base import Wrapper
"""
Code adapted from OpenAI Baslines, under the following license.
The MIT License
Copyright (c) 2017 OpenAI (http://openai.com)
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in
all copies or substantial portions of the Software.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN
THE SOFTWARE.
"""
class RunningMeanStd(object):
# https://en.wikipedia.org/wiki/Algorithms_for_calculating_variance#Parallel_algorithm
def __init__(self, epsilon=1e-4, shape=()):
self.mean = np.zeros(shape, 'float64')
self.var = np.ones(shape, 'float64')
self.count = epsilon
def update(self, x):
batch_mean = np.mean(x, axis=0)
batch_var = np.var(x, axis=0)
batch_count = x.shape[0]
self.update_from_moments(batch_mean, batch_var, batch_count)
def update_from_moments(self, batch_mean, batch_var, batch_count):
self.mean, self.var, self.count = update_mean_var_count_from_moments(
self.mean,
self.var,
self.count,
batch_mean,
batch_var,
batch_count)
def update_mean_var_count_from_moments(mean,
var,
count,
batch_mean,
batch_var,
batch_count):
delta = batch_mean - mean
tot_count = count + batch_count
new_mean = mean + delta * batch_count / tot_count
m_a = var * count
m_b = batch_var * batch_count
M2 = m_a + m_b + np.square(delta) * count * batch_count / tot_count
new_var = M2 / tot_count
new_count = tot_count
return new_mean, new_var, new_count
class Normalizer(Wrapper):
"""
[[Source]](https://github.com/seba-1511/cherry/blob/master/cherry/envs/normalizer_wrapper.py)
**Description**
Normalizes the states and rewards with a running average.
**Arguments**
* **env** (Environment) - Environment to normalize.
* **states** (bool, *optional*, default=True) - Whether to normalize the
states.
* **rewards** (bool, *optional*, default=True) - Whether to normalize the
rewards.
* **clip_states** (bool, *optional*, default=10.0) - Clip each state
dimension between [-clip_states, clip_states].
* **clip_rewards** (float, *optional*, default=10.0) - Clip rewards
between [-clip_rewards, clip_rewards].
* **gamma** (float, *optional*, default=0.99) - Discount factor for
rewards running averages.
* **eps** (float, *optional*, default=1e-8) - Numerical stability.
**Credit**
Adapted from OpenAI's baselines implementation.
**Example**
~~~python
env = gym.make('CartPole-v0')
env = cherry.envs.Normalizer(env,
states=True,
rewards=False)
~~~
"""
def __init__(self,
env,
states=True,
rewards=True,
clip_states=10.0,
clip_rewards=10.0,
gamma=0.99,
eps=1e-8):
Wrapper.__init__(self, env)
self.env = env
self.eps = eps
self.gamma = gamma
self.clipob = clip_states
self.cliprew = clip_rewards
self.ret = np.zeros(1)
if states:
self.state_rms = RunningMeanStd(shape=self.observation_space.shape)
self.ret_rms = RunningMeanStd(shape=()) if rewards else None
def _obfilt(self, state):
if self.state_rms:
self.state_rms.update(state)
centered = state - self.state_rms.mean
std = np.sqrt(self.state_rms.var + self.eps)
obs = np.clip(centered / std, -self.clipob, self.clipob)
return obs
def reset(self):
self.ret = np.zeros(1)
state = self.env.reset()
return self._obfilt(state)
def step(self, action):
state, reward, done, info = self.env.step(action)
self.ret = self.gamma * self.ret + reward
state = self._obfilt(state)
if self.ret_rms:
reward = np.array([[reward]])
self.ret_rms.update(self.ret)
std = np.sqrt(self.ret_rms.var + self.eps)
reward = np.clip(reward / std, -self.cliprew, self.cliprew)[0, 0]
if self.is_vectorized:
self.ret = self.ret * (1.0 - done)
else:
self.ret = self.ret * 0.0
return state, reward, done, info
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