ARS.py

# Augmented Random Search (ARS) AI

 # Importing the libraries 
import os
import numpy as np

 
# Setting the hyper Parameters 
class Hp():
    
    def __init__(self):
        self.nb_steps = 1000
        self.episode_length = 1000
        self.learning_rate = 0.02
        self.nb_directions = 16
        self.nb_best_directions = 16
        assert self.nb_best_directions <= self.nb_directions
        self.noise = 0.03
        self.seed = 1
        self.env_name = ''
        
# Normalizing the states 
class Normalizer():
    
    def __init__(self, nb_inputs):
        self.n = np.zeros(nb_inputs)
        self.mean = np.zeros(nb_inputs)
        self.mean_diff = np.zeros(nb_inputs)
        self.var = np.zeros(nb_inputs)
        
    def observe(self, x):
        self.n += 1
        last_mean = self.mean.copy()
        self.mean += (x- self.mean) / self.n
        self.mean_diff += (x - last_mean) * (x - self.mean)
        self.var = (self.mean_diff / self.n).clip(min = 1e-2)
        
    def normalize(self, inputs):
        obs_mean = self.mean
        obs_std = np.sqrt(self.var)
        return (inputs - obs_mean) / obs_std