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preprocessor.py
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preprocessor.py
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#!/usr/bin/env python3
# -*- coding: utf-8 -*-
# =====================================
# @Time : 2020/8/10
# @Author : Yang Guan (Tsinghua Univ.)
# @FileName: preprocessor.py
# =====================================
import numpy as np
import tensorflow as tf
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 RunningMeanStd(object):
def __init__(self, epsilon=1e-4, shape=()):
self.mean = np.zeros(shape, dtype=np.float32)
self.var = np.ones(shape, dtype=np.float32)
self.count = epsilon
self.tf_mean = tf.Variable(tf.zeros(shape), dtype=tf.float32, trainable=False)
self.tf_var = tf.Variable(tf.ones(shape), dtype=tf.float32, trainable=False)
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)
self.tf_mean.assign(tf.constant(self.mean))
self.tf_var.assign(tf.constant(self.var))
def set_params(self, mean, var, count):
self.mean = mean
self.var = var
self.count = count
self.tf_mean.assign(tf.constant(self.mean))
self.tf_var.assign(tf.constant(self.var))
def get_params(self, ):
return self.mean, self.var, self.count
class Preprocessor(object):
def __init__(self, obs_dim, obs_ptype=None, rew_ptype=None, obs_scale=None,
rew_scale=None, rew_shift=None, clipob=10., cliprew=10., gamma=0.99, epsilon=1e-8, **kwargs):
self.obs_ptype = obs_ptype
self.ob_rms = RunningMeanStd(shape=(obs_dim,)) if self.obs_ptype == 'normalize' else None
self.rew_ptype = rew_ptype
self.ret_rms = RunningMeanStd(shape=()) if self.rew_ptype == 'normalize' else None
self.obs_scale = np.array(obs_scale) if self.obs_ptype == 'scale' else None
self.rew_scale = rew_scale if self.rew_ptype == 'scale' else None
self.rew_shift = rew_shift if self.rew_ptype == 'scale' else None
self.clipob = clipob
self.cliprew = cliprew
self.gamma = gamma
self.epsilon = epsilon
self.num_agent = None
if 'num_agent' in kwargs.keys():
self.ret = np.zeros(kwargs['num_agent'])
self.num_agent = kwargs['num_agent']
else:
self.ret = 0
def process_rew(self, rew, done):
if self.rew_ptype == 'normalize':
if self.num_agent is not None:
self.ret = self.ret * self.gamma + rew
self.ret_rms.update(self.ret)
rew = np.clip(rew / np.sqrt(self.ret_rms.var + self.epsilon), -self.cliprew, self.cliprew)
self.ret = np.where(done == 1, np.zeros(self.ret), self.ret)
else:
self.ret = self.ret * self.gamma + rew
self.ret_rms.update(np.array([self.ret]))
rew = np.clip(rew / np.sqrt(self.ret_rms.var + self.epsilon), -self.cliprew, self.cliprew)
self.ret = 0 if done else self.ret
return rew
elif self.rew_ptype == 'scale':
return (rew + self.rew_shift) * self.rew_scale
else:
return rew
def process_obs(self, obs):
if self.obs_ptype == 'normalize':
if self.num_agent is not None:
self.ob_rms.update(obs)
obs = np.clip((obs - self.ob_rms.mean) / np.sqrt(self.ob_rms.var + self.epsilon), -self.clipob, self.clipob)
return obs
else:
self.ob_rms.update(np.array([obs]))
obs = np.clip((obs - self.ob_rms.mean) / np.sqrt(self.ob_rms.var + self.epsilon), -self.clipob,
self.clipob)
return obs
elif self.obs_ptype == 'scale':
return obs * self.obs_scale
else:
return obs
def np_process_obses(self, obses):
if self.obs_ptype == 'normalize':
obses = np.clip((obses - self.ob_rms.mean) / np.sqrt(self.ob_rms.var + self.epsilon), -self.clipob, self.clipob)
return obses
elif self.obs_ptype == 'scale':
return obses * self.obs_scale
else:
return obses
def np_process_rewards(self, rewards):
if self.rew_ptype == 'normalize':
rewards = np.clip(rewards / np.sqrt(self.ret_rms.var + self.epsilon), -self.cliprew, self.cliprew)
return rewards
elif self.rew_ptype == 'scale':
return (rewards + self.rew_shift) * self.rew_scale
else:
return rewards
def tf_process_obses(self, obses):
with tf.name_scope('obs_process') as scope:
if self.obs_ptype == 'normalize':
obses = tf.clip_by_value(
(obses - self.ob_rms.tf_mean) / tf.sqrt(self.ob_rms.tf_var + tf.constant(self.epsilon)),
-self.clipob,
self.clipob)
return obses
elif self.obs_ptype == 'scale':
return obses * tf.convert_to_tensor(self.obs_scale, dtype=tf.float32)
else:
return tf.convert_to_tensor(obses, dtype=tf.float32)
def tf_process_rewards(self, rewards):
with tf.name_scope('reward_process') as scope:
if self.rew_ptype == 'normalize':
rewards = tf.clip_by_value(rewards / tf.sqrt(self.ret_rms.tf_var + tf.constant(self.epsilon)),
-self.cliprew,
self.cliprew)
return rewards
elif self.rew_ptype == 'scale':
return (rewards+tf.convert_to_tensor(self.rew_shift, dtype=tf.float32)) \
* tf.convert_to_tensor(self.rew_scale, dtype=tf.float32)
else:
return tf.convert_to_tensor(rewards, dtype=tf.float32)
def set_params(self, params):
if self.ob_rms:
self.ob_rms.set_params(*params['ob_rms'])
if self.ret_rms:
self.ret_rms.set_params(*params['ret_rms'])
def get_params(self):
tmp = {}
if self.ob_rms:
tmp.update({'ob_rms': self.ob_rms.get_params()})
if self.ret_rms:
tmp.update({'ret_rms': self.ret_rms.get_params()})
return tmp
def save_params(self, save_dir):
np.save(save_dir + '/ppc_params.npy', self.get_params())
def load_params(self, load_dir):
params = np.load(load_dir + '/ppc_params.npy', allow_pickle=True)
params = params.item()
self.set_params(params)