mountain_car.py

# -*- coding: utf-8 -*-
"""
@author: Olivier Sigaud

A merge between two sources:

* Adaptation of the MountainCar Environment from the "FAReinforcement" library
of Jose Antonio Martin H. (version 1.0), adapted by  'Tom Schaul, tom@idsia.ch'
and then modified by Arnaud de Broissia

* the OpenAI/gym MountainCar environment
itself from
http://incompleteideas.net/sutton/MountainCar/MountainCar1.cp
permalink: https://perma.cc/6Z2N-PFWC
"""

import math

import numpy as np

class Continuous_MountainCarEnv:

    def __init__(self, goal_velocity = 0):
        self.min_action = -1.0
        self.max_action = 1.0
        self.min_position = -1.2
        self.max_position = 0.6
        self.max_speed = 0.07
        self.goal_position = 0.45 # was 0.5 in gym, 0.45 in Arnaud de Broissia's version
        self.goal_velocity = goal_velocity
        self.power = 0.0015

        self.low_state = np.array([self.min_position, -self.max_speed])
        self.high_state = np.array([self.max_position, self.max_speed])

        self.seed()
        self.reset()

    def seed(self, seed=None):
        np.random.seed(seed)

    def step(self, action):

        position = self.state[0]
        velocity = self.state[1]
        force = min(max(action[0], -1.0), 1.0)

        velocity += force*self.power -0.0025 * math.cos(3*position)
        if (velocity > self.max_speed): velocity = self.max_speed
        if (velocity < -self.max_speed): velocity = -self.max_speed
        position += velocity
        if (position > self.max_position): position = self.max_position
        if (position < self.min_position): position = self.min_position
        if (position==self.min_position and velocity<0): velocity = 0

        done = bool(position >= self.goal_position and velocity >= self.goal_velocity)

        reward = 0
        if done:
            reward = 100.0
        reward-= math.pow(action[0],2)*0.1

        self.state = np.array([position, velocity])
        return self.state, reward, done, {}

    def reset(self):
        self.state = np.array([np.random.uniform(low=-0.6, high=-0.4), 0])
        return np.array(self.state)

    # def print(self):
    #     print(self.state)