class_docstr: ### Description
  
  The Mountain Car MDP is a deterministic MDP that consists of a car placed stochastically
  at the bottom of a sinusoidal valley, with the only possible actions being the accelerations
  that can be applied to the car in either direction. The goal of the MDP is to strategically
  accelerate the car to reach the goal state on top of the right hill. There are two versions
  of the mountain car domain in gym: one with discrete actions and one with continuous.
  This version is the one with discrete actions.
  
  This MDP first appeared in [Andrew Moore's PhD Thesis (1990)](https://www.cl.cam.ac.uk/techreports/UCAM-CL-TR-209.pdf)
  
  ```
  @TECHREPORT{Moore90efficientmemory-based,
      author = {Andrew William Moore},
      title = {Efficient Memory-based Learning for Robot Control},
      institution = {University of Cambridge},
      year = {1990}
  }
  ```
  
  ### Observation Space
  
  The observation is a `ndarray` with shape `(2,)` where the elements correspond to the following:
  
  | Num | Observation                          | Min  | Max | Unit         |
  |-----|--------------------------------------|------|-----|--------------|
  | 0   | position of the car along the x-axis | -Inf | Inf | position (m) |
  | 1   | velocity of the car                  | -Inf | Inf | position (m) |
  
  ### Action Space
  
  There are 3 discrete deterministic actions:
  
  | Num | Observation             | Value | Unit         |
  |-----|-------------------------|-------|--------------|
  | 0   | Accelerate to the left  | Inf   | position (m) |
  | 1   | Don't accelerate        | Inf   | position (m) |
  | 2   | Accelerate to the right | Inf   | position (m) |
  
  ### Transition Dynamics:
  
  Given an action, the mountain car follows the following transition dynamics:
  
  *velocity<sub>t+1</sub> = velocity<sub>t</sub> + (action - 1) * force - cos(3 * position<sub>t</sub>) * gravity*
  
  *position<sub>t+1</sub> = position<sub>t</sub> + velocity<sub>t+1</sub>*
  
  where force = 0.001 and gravity = 0.0025. The collisions at either end are inelastic with the velocity set to 0
  upon collision with the wall. The position is clipped to the range `[-1.2, 0.6]` and
  velocity is clipped to the range `[-0.07, 0.07]`.
  
  
  ### Reward:
  
  The goal is to reach the flag placed on top of the right hill as quickly as possible, as such the agent is
  penalised with a reward of -1 for each timestep.
  
  ### Starting State
  
  The position of the car is assigned a uniform random value in *[-0.6 , -0.4]*.
  The starting velocity of the car is always assigned to 0.
  
  ### Episode End
  
  The episode ends if either of the following happens:
  1. Termination: The position of the car is greater than or equal to 0.5 (the goal position on top of the right hill)
  2. Truncation: The length of the episode is 200.
  
  
  ### Arguments
  
  ```
  gym.make('MountainCar-v0')
  ```
  
  ### Version History
  
  * v0: Initial versions release (1.0.0)
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code: def _height(self, xs):
      return np.sin(3 * xs) * 0.45 + 0.55
  
file_path: gym/gym/envs/classic_control/mountain_car.py
imports: import math
  from typing import Optional
  import numpy
  import gym
  from gym import spaces
  from gym.envs.classic_control import utils
  from gym.error import DependencyNotInstalled
label_desc: Use image node_id 4 for calling the MountainCarEnv obj's underlying member method code with example usage: obj._height(xs) and returns: unknown