utils.py

import numpy as np
import gym
from collections import deque
import random

class OUNoise(object):

    def __init__(self, action_space, mu=0.0, theta=0.15, max_sigma=0.3, min_sigma=0.3,
                decay_period=100000):

        self.mu = mu
        self.theta = theta
        self.sigma = max_sigma
        self.max_sigma = max_sigma
        self.min_sigma = min_sigma
        self.decay_period = decay_period
        self.action_dim = action_space.shape[0]
        self.low = action_space.low
        self.high = action_space.high
        self.reset()

    def reset(self):
        self.state = np.ones(self.action_dim)*self.mu

    def evolve_state(self):
        x = self.state
        dx = self.theta * (self.mu - x) + self.sigma*np.random.randn(self.action_dim)
        self.state = x + dx
        return self.state

    def get_action(self, action, t=0):
        ou_state = self.evolve_state()
        self.sigma = self.max_sigma - (self.max_sigma - self.min_sigma)*min(1.0, t/self.decay_period)
        return  np.clip(action+ou_state, self.low, self.high)



class NormalizedEnv(gym.ActionWrapper):

    def action(self, action):
        act_k = (self.action_space.high - self.action_space.low)/ 2.
        act_b = (self.action_space.high + self.action_space.low)/ 2.
        return act_k*action + act_b

    def reverse_action(self, action):
        act_k_inv = 2./(self.action_space.high - self.action_space.low)
        act_b = (self.action_space.high + self.action_space.low)/ 2.
        return act_k_inv*(action - act_b)