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bf093c6890
* Remove additional ignores from flake8 * Remove all unused imports * Remove all unused imports * Update flake8 and pyupgrade * F841, removed unused variables * E731, removed lambda assignment to variables * Remove E731, F403, F405, F524 * Remove E722, bare exceptions * Remove E712, compare variable == True or == False to is True or is False * Remove E402, module level import not at top of file * Added --pre-file-ignores * Add --per-file-ignores removing E741, E302 and E704 * Add E741, do not use variables named ‘l’, ‘O’, or ‘I’ to ignore issues in classic control * Fixed issues for pytest==6.2 * Remove unnecessary # noqa * Edit comment with the removal of E302 * Added warnings and declared module, attr for pyright type hinting * Remove unused import * Removed flake8 E302 * Updated flake8 from 3.9.2 to 4.0.1 * Remove unused variable
250 lines
8.8 KiB
Python
250 lines
8.8 KiB
Python
"""
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http://incompleteideas.net/MountainCar/MountainCar1.cp
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permalink: https://perma.cc/6Z2N-PFWC
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"""
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import math
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from typing import Optional
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import numpy as np
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import gym
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from gym import spaces
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class MountainCarEnv(gym.Env):
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"""
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### Description
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The Mountain Car MDP is a deterministic MDP that consists of a car placed stochastically
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at the bottom of a sinusoidal valley, with the only possible actions being the accelerations
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that can be applied to the car in either direction. The goal of the MDP is to strategically
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accelerate the car to reach the goal state on top of the right hill. There are two versions
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of the mountain car domain in gym: one with discrete actions and one with continuous.
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This version is the one with discrete actions.
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This MDP first appeared in [Andrew Moore's PhD Thesis (1990)](https://www.cl.cam.ac.uk/techreports/UCAM-CL-TR-209.pdf)
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```
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@TECHREPORT{Moore90efficientmemory-based,
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author = {Andrew William Moore},
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title = {Efficient Memory-based Learning for Robot Control},
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institution = {University of Cambridge},
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year = {1990}
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}
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```
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### Observation Space
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The observation is a `ndarray` with shape `(2,)` where the elements correspond to the following:
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| Num | Observation | Min | Max | Unit |
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|-----|-------------------------------------------------------------|--------------------|--------|------|
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| 0 | position of the car along the x-axis | -Inf | Inf | position (m) |
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| 1 | velocity of the car | -Inf | Inf | position (m) |
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### Action Space
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There are 3 discrete deterministic actions:
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| Num | Observation | Value | Unit |
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|-----|-------------------------------------------------------------|---------|------|
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| 0 | Accelerate to the left | Inf | position (m) |
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| 1 | Don't accelerate | Inf | position (m) |
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| 2 | Accelerate to the right | Inf | position (m) |
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### Transition Dynamics:
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Given an action, the mountain car follows the following transition dynamics:
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*velocity<sub>t+1</sub> = velocity<sub>t</sub> + (action - 1) * force - cos(3 * position<sub>t</sub>) * gravity*
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*position<sub>t+1</sub> = position<sub>t</sub> + velocity<sub>t+1</sub>*
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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]`.
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### Reward:
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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 it isn't at the goal and is not penalised (reward = 0) for when it reaches the goal.
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### Starting State
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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.
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### Episode Termination
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The episode terminates if either of the following happens:
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1. The position of the car is greater than or equal to 0.5 (the goal position on top of the right hill)
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2. The length of the episode is 200.
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### Arguments
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```
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gym.make('MountainCar-v0')
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```
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### Version History
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* v0: Initial versions release (1.0.0)
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"""
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metadata = {"render_modes": ["human", "rgb_array"], "render_fps": 30}
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def __init__(self, goal_velocity=0):
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self.min_position = -1.2
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self.max_position = 0.6
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self.max_speed = 0.07
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self.goal_position = 0.5
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self.goal_velocity = goal_velocity
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self.force = 0.001
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self.gravity = 0.0025
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self.low = np.array([self.min_position, -self.max_speed], dtype=np.float32)
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self.high = np.array([self.max_position, self.max_speed], dtype=np.float32)
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self.screen = None
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self.clock = None
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self.isopen = True
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self.action_space = spaces.Discrete(3)
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self.observation_space = spaces.Box(self.low, self.high, dtype=np.float32)
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def step(self, action: int):
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assert self.action_space.contains(
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action
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), f"{action!r} ({type(action)}) invalid"
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position, velocity = self.state
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velocity += (action - 1) * self.force + math.cos(3 * position) * (-self.gravity)
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velocity = np.clip(velocity, -self.max_speed, self.max_speed)
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position += velocity
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position = np.clip(position, self.min_position, self.max_position)
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if position == self.min_position and velocity < 0:
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velocity = 0
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done = bool(position >= self.goal_position and velocity >= self.goal_velocity)
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reward = -1.0
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self.state = (position, velocity)
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return np.array(self.state, dtype=np.float32), reward, done, {}
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def reset(
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self,
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*,
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seed: Optional[int] = None,
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return_info: bool = False,
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options: Optional[dict] = None,
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):
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super().reset(seed=seed)
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self.state = np.array([self.np_random.uniform(low=-0.6, high=-0.4), 0])
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if not return_info:
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return np.array(self.state, dtype=np.float32)
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else:
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return np.array(self.state, dtype=np.float32), {}
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def _height(self, xs):
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return np.sin(3 * xs) * 0.45 + 0.55
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def render(self, mode="human"):
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import pygame
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from pygame import gfxdraw
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screen_width = 600
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screen_height = 400
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world_width = self.max_position - self.min_position
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scale = screen_width / world_width
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carwidth = 40
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carheight = 20
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if self.screen is None:
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pygame.init()
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pygame.display.init()
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self.screen = pygame.display.set_mode((screen_width, screen_height))
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if self.clock is None:
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self.clock = pygame.time.Clock()
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self.surf = pygame.Surface((screen_width, screen_height))
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self.surf.fill((255, 255, 255))
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pos = self.state[0]
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xs = np.linspace(self.min_position, self.max_position, 100)
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ys = self._height(xs)
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xys = list(zip((xs - self.min_position) * scale, ys * scale))
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pygame.draw.aalines(self.surf, points=xys, closed=False, color=(0, 0, 0))
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clearance = 10
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l, r, t, b = -carwidth / 2, carwidth / 2, carheight, 0
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coords = []
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for c in [(l, b), (l, t), (r, t), (r, b)]:
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c = pygame.math.Vector2(c).rotate_rad(math.cos(3 * pos))
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coords.append(
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(
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c[0] + (pos - self.min_position) * scale,
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c[1] + clearance + self._height(pos) * scale,
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)
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)
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gfxdraw.aapolygon(self.surf, coords, (0, 0, 0))
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gfxdraw.filled_polygon(self.surf, coords, (0, 0, 0))
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for c in [(carwidth / 4, 0), (-carwidth / 4, 0)]:
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c = pygame.math.Vector2(c).rotate_rad(math.cos(3 * pos))
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wheel = (
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int(c[0] + (pos - self.min_position) * scale),
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int(c[1] + clearance + self._height(pos) * scale),
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)
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gfxdraw.aacircle(
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self.surf, wheel[0], wheel[1], int(carheight / 2.5), (128, 128, 128)
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)
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gfxdraw.filled_circle(
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self.surf, wheel[0], wheel[1], int(carheight / 2.5), (128, 128, 128)
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)
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flagx = int((self.goal_position - self.min_position) * scale)
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flagy1 = int(self._height(self.goal_position) * scale)
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flagy2 = flagy1 + 50
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gfxdraw.vline(self.surf, flagx, flagy1, flagy2, (0, 0, 0))
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gfxdraw.aapolygon(
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self.surf,
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[(flagx, flagy2), (flagx, flagy2 - 10), (flagx + 25, flagy2 - 5)],
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(204, 204, 0),
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)
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gfxdraw.filled_polygon(
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self.surf,
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[(flagx, flagy2), (flagx, flagy2 - 10), (flagx + 25, flagy2 - 5)],
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(204, 204, 0),
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)
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self.surf = pygame.transform.flip(self.surf, False, True)
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self.screen.blit(self.surf, (0, 0))
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if mode == "human":
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pygame.event.pump()
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self.clock.tick(self.metadata["render_fps"])
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pygame.display.flip()
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if mode == "rgb_array":
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return np.transpose(
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np.array(pygame.surfarray.pixels3d(self.screen)), axes=(1, 0, 2)
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)
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else:
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return self.isopen
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def get_keys_to_action(self):
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# Control with left and right arrow keys.
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return {(): 1, (276,): 0, (275,): 2, (275, 276): 1}
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def close(self):
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if self.screen is not None:
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import pygame
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pygame.display.quit()
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pygame.quit()
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self.isopen = False
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