text: """This file is based on the car_racing.py in OpenAI's gym.
  
  Description
      We provide 3 modified CarRacing environments here:
      1. CarRacingColor-v0
          Both the lane and the grass colors are perturbed at the env.reset().
      2. CarRacingBar-v0
          We add vertical bars on the left and right side of the screen.
      3. CarRacingBlob-v0
          A red blob that follows the car at a fixed position in the car's frame.
      4. CarRacingNoise-v0
          We replace the green background with noise.
      5. CarRacingVideo-v0-xxx
          We replace the green background with frames from a video, the user is
          responsible for creating these frames and pass the directory as xxx
          in gym.make().
      These environments were first used in the paper "Neuroevolution of Self-Interpretable Agent"
      https://attentionagent.github.io/
  
  Author
      Yujin Tang (yujintang@google.com)
  """
  
  import sys, math
  import numpy as np
  
  import Box2D
  from Box2D.b2 import (edgeShape, circleShape, fixtureDef, polygonShape, revoluteJointDef, contactListener)
  
  import gym
  from gym import spaces
  from .car_dynamics import Car
  from gym.utils import colorize, seeding, EzPickle
  
  import pyglet
  from pyglet import gl
  
  # Easiest continuous control task to learn from pixels, a top-down racing environment.
  # Discrete control is reasonable in this environment as well, on/off discretization is
  # fine.
  #
  # State consists of STATE_W x STATE_H pixels.
  #
  # Reward is -0.1 every frame and +1000/N for every track tile visited, where N is
  # the total number of tiles visited in the track. For example, if you have finished in 732 frames,
  # your reward is 1000 - 0.1*732 = 926.8 points.
  #
  # Game is solved when agent consistently gets 900+ points. Track generated is random every episode.
  #
  # Episode finishes when all tiles are visited. Car also can go outside of PLAYFIELD, that
  # is far off the track, then it will get -100 and die.
  #
  # Some indicators shown at the bottom of the window and the state RGB buffer. From
  # left to right: true speed, four ABS sensors, steering wheel position and gyroscope.
  #
  # To play yourself (it's rather fast for humans), type:
  #
  # python gym/envs/box2d/car_racing.py
  #
  # Remember it's powerful rear-wheel drive car, don't press accelerator and turn at the
  # same time.
  #
  # Created by Oleg Klimov. Licensed on the same terms as the rest of OpenAI Gym.
  
  STATE_W = 96   # less than Atari 160x192
  STATE_H = 96
  VIDEO_W = 600
  VIDEO_H = 400
  WINDOW_W = 1000
  WINDOW_H = 800
  
  SCALE       = 6.0        # Track scale
  TRACK_RAD   = 900/SCALE  # Track is heavily morphed circle with this radius
  PLAYFIELD   = 2000/SCALE # Game over boundary
  FPS         = 50         # Frames per second
  ZOOM        = 2.7        # Camera zoom
  ZOOM_FOLLOW = True       # Set to False for fixed view (don't use zoom)
  
  
  TRACK_DETAIL_STEP = 21/SCALE
  TRACK_TURN_RATE = 0.31
  TRACK_WIDTH = 40/SCALE
  BORDER = 8/SCALE
  BORDER_MIN_COUNT = 4
  
  ROAD_COLOR = [0.4, 0.4, 0.4]
  
  class FrictionDetector(contactListener):
      def __init__(self, env):
          contactListener.__init__(self)
          self.env = env
      def BeginContact(self, contact):
          self._contact(contact, True)
      def EndContact(self, contact):
          self._contact(contact, False)
      def _contact(self, contact, begin):
          tile = None
          obj = None
          u1 = contact.fixtureA.body.userData
          u2 = contact.fixtureB.body.userData
          if u1 and "road_friction" in u1.__dict__:
              tile = u1
              obj  = u2
          if u2 and "road_friction" in u2.__dict__:
              tile = u2
              obj  = u1
          if not tile:
              return
  
          tile.color[0] = self.env.road_color[0]
          tile.color[1] = self.env.road_color[1]
          tile.color[2] = self.env.road_color[2]
          if not obj or "tiles" not in obj.__dict__:
              return
          if begin:
              obj.tiles.add(tile)
              # print tile.road_friction, "ADD", len(obj.tiles)
              if not tile.road_visited:
                  tile.road_visited = True
                  self.env.reward += 1000.0/len(self.env.track)
                  self.env.tile_visited_count += 1
          else:
              obj.tiles.remove(tile)
              # print tile.road_friction, "DEL", len(obj.tiles) -- should delete to zero when on grass (this works)
  
  class CarRacing(gym.Env, EzPickle):
      metadata = {
          'render.modes': ['human', 'rgb_array', 'state_pixels'],
          'video.frames_per_second' : FPS
      }
  
      def __init__(self, verbose=1, **kwargs):
          EzPickle.__init__(self)
          self.seed()
  
          self.road_color = ROAD_COLOR[:]
          self.grass_color = [0.4, 0.8, 0.4, 1]
          if 'modification' in kwargs:
            self._modification_type = kwargs['modification']
          else:
            self._modification_type = ''
  
          self.contactListener_keepref = FrictionDetector(self)
          self.world = Box2D.b2World((0,0), contactListener=self.contactListener_keepref)
          self.viewer = None
          self.invisible_state_window = None
          self.invisible_video_window = None
          self.road = None
          self.car = None
          self.reward = 0.0
          self.prev_reward = 0.0
          self.verbose = verbose
          self.fd_tile = fixtureDef(
                  shape = polygonShape(vertices=
                      [(0, 0),(1, 0),(1, -1),(0, -1)]))
  
          self.action_space = spaces.Box( np.array([-1,0,0]), np.array([+1,+1,+1]), dtype=np.float32)  # steer, gas, brake
          self.observation_space = spaces.Box(low=0, high=255, shape=(STATE_H, STATE_W, 3), dtype=np.uint8)
  
          self.step_cnt = 0
  
      def seed(self, seed=None):
          self.np_random, seed = seeding.np_random(seed)
          return [seed]
  
      def _destroy(self):
          if not self.road:
              return
          for t in self.road:
              self.world.DestroyBody(t)
          self.road = []
          self.car.destroy()
  
      def _create_track(self):
          CHECKPOINTS = 12
  
          # Create checkpoints
          checkpoints = []
          for c in range(CHECKPOINTS):
              alpha = 2*math.pi*c/CHECKPOINTS + self.np_random.uniform(0, 2*math.pi*1/CHECKPOINTS)
              rad = self.np_random.uniform(TRACK_RAD/3, TRACK_RAD)
              if c==0:
                  alpha = 0
                  rad = 1.5*TRACK_RAD
              if c==CHECKPOINTS-1:
                  alpha = 2*math.pi*c/CHECKPOINTS
                  self.start_alpha = 2*math.pi*(-0.5)/CHECKPOINTS
                  rad = 1.5*TRACK_RAD
              # # YUJIN
              # rad = 1 * TRACK_RAD
              # alpha = 2 * math.pi * c / CHECKPOINTS
              # self.start_alpha = 2*math.pi*(-0.5)/CHECKPOINTS
              checkpoints.append( (alpha, rad*math.cos(alpha), rad*math.sin(alpha)) )
  
          # print "\n".join(str(h) for h in checkpoints)
          # self.road_poly = [ (    # uncomment this to see checkpoints
          #    [ (tx,ty) for a,tx,ty in checkpoints ],
          #    (0.7,0.7,0.9) ) ]
          self.road = []
  
          # Go from one checkpoint to another to create track
          x, y, beta = 1.5*TRACK_RAD, 0, 0
          dest_i = 0
          laps = 0
          track = []
          no_freeze = 2500
          visited_other_side = False
          while True:
              alpha = math.atan2(y, x)
              if visited_other_side and alpha > 0:
                  laps += 1
                  visited_other_side = False
              if alpha < 0:
                  visited_other_side = True
                  alpha += 2*math.pi
              while True: # Find destination from checkpoints
                  failed = True
                  while True:
                      dest_alpha, dest_x, dest_y = checkpoints[dest_i % len(checkpoints)]
                      if alpha <= dest_alpha:
                          failed = False
                          break
                      dest_i += 1
                      if dest_i % len(checkpoints) == 0:
                          break
                  if not failed:
                      break
                  alpha -= 2*math.pi
                  continue
              r1x = math.cos(beta)
              r1y = math.sin(beta)
              p1x = -r1y
              p1y = r1x
              dest_dx = dest_x - x  # vector towards destination
              dest_dy = dest_y - y
              proj = r1x*dest_dx + r1y*dest_dy  # destination vector projected on rad
              while beta - alpha >  1.5*math.pi:
                   beta -= 2*math.pi
              while beta - alpha < -1.5*math.pi:
                   beta += 2*math.pi
              prev_beta = beta
              proj *= SCALE
              if proj >  0.3:
                   beta -= min(TRACK_TURN_RATE, abs(0.001*proj))
              if proj < -0.3:
                   beta += min(TRACK_TURN_RATE, abs(0.001*proj))
              x += p1x*TRACK_DETAIL_STEP
              y += p1y*TRACK_DETAIL_STEP
              track.append( (alpha,prev_beta*0.5 + beta*0.5,x,y) )
              if laps > 4:
                   break
              no_freeze -= 1
              if no_freeze==0:
                   break
          # print "\n".join([str(t) for t in enumerate(track)])
  
          # Find closed loop range i1..i2, first loop should be ignored, second is OK
          i1, i2 = -1, -1
          i = len(track)
          while True:
              i -= 1
              if i==0:
                  return False  # Failed
              pass_through_start = track[i][0] > self.start_alpha and track[i-1][0] <= self.start_alpha
              if pass_through_start and i2==-1:
                  i2 = i
              elif pass_through_start and i1==-1:
                  i1 = i
                  break
          if self.verbose == 1:
              print("Track generation: %i..%i -> %i-tiles track" % (i1, i2, i2-i1))
          assert i1!=-1
          assert i2!=-1
  
          track = track[i1:i2-1]
  
          first_beta = track[0][1]
          first_perp_x = math.cos(first_beta)
          first_perp_y = math.sin(first_beta)
          # Length of perpendicular jump to put together head and tail
          well_glued_together = np.sqrt(
              np.square( first_perp_x*(track[0][2] - track[-1][2]) ) +
              np.square( first_perp_y*(track[0][3] - track[-1][3]) ))
          if well_glued_together > TRACK_DETAIL_STEP:
              return False
  
          # Red-white border on hard turns
          border = [False]*len(track)
          for i in range(len(track)):
              good = True
              oneside = 0
              for neg in range(BORDER_MIN_COUNT):
                  beta1 = track[i-neg-0][1]
                  beta2 = track[i-neg-1][1]
                  good &= abs(beta1 - beta2) > TRACK_TURN_RATE*0.2
                  oneside += np.sign(beta1 - beta2)
              good &= abs(oneside) == BORDER_MIN_COUNT
              border[i] = good
          for i in range(len(track)):
              for neg in range(BORDER_MIN_COUNT):
                  border[i-neg] |= border[i]
  
          # Create tiles
          for i in range(len(track)):
              alpha1, beta1, x1, y1 = track[i]
              alpha2, beta2, x2, y2 = track[i-1]
              road1_l = (x1 - TRACK_WIDTH*math.cos(beta1), y1 - TRACK_WIDTH*math.sin(beta1))
              road1_r = (x1 + TRACK_WIDTH*math.cos(beta1), y1 + TRACK_WIDTH*math.sin(beta1))
              road2_l = (x2 - TRACK_WIDTH*math.cos(beta2), y2 - TRACK_WIDTH*math.sin(beta2))
              road2_r = (x2 + TRACK_WIDTH*math.cos(beta2), y2 + TRACK_WIDTH*math.sin(beta2))
              vertices = [road1_l, road1_r, road2_r, road2_l]
              self.fd_tile.shape.vertices = vertices
              t = self.world.CreateStaticBody(fixtures=self.fd_tile)
              t.userData = t
              c = 0.01*(i%3)
              t.color = [self.road_color[0] + c,
                         self.road_color[1] + c,
                         self.road_color[2] + c]
              t.road_visited = False
              t.road_friction = 1.0
              t.fixtures[0].sensor = True
              self.road_poly.append(( [road1_l, road1_r, road2_r, road2_l], t.color ))
  
              # # YUJIN: add a fake lane
              # offset = -20
              # froad1_l = (x1 - (offset+TRACK_WIDTH)*math.cos(beta1), y1 - (offset+TRACK_WIDTH)*math.sin(beta1))
              # froad1_r = (x1 + (-offset+TRACK_WIDTH)*math.cos(beta1), y1 + (-offset+TRACK_WIDTH)*math.sin(beta1))
              # froad2_l = (x2 - (offset+TRACK_WIDTH)*math.cos(beta2), y2 - (offset+TRACK_WIDTH)*math.sin(beta2))
              # froad2_r = (x2 + (-offset+TRACK_WIDTH)*math.cos(beta2), y2 + (-offset+TRACK_WIDTH)*math.sin(beta2))
              # r_color = ROAD_COLOR[:]
              # r_color[2] += 0.1
              # self.froad_poly.append(( [froad1_l, froad1_r, froad2_r, froad2_l], r_color))
  
              self.road.append(t)
              if border[i]:
                  side = np.sign(beta2 - beta1)
                  b1_l = (x1 + side* TRACK_WIDTH        *math.cos(beta1), y1 + side* TRACK_WIDTH        *math.sin(beta1))
                  b1_r = (x1 + side*(TRACK_WIDTH+BORDER)*math.cos(beta1), y1 + side*(TRACK_WIDTH+BORDER)*math.sin(beta1))
                  b2_l = (x2 + side* TRACK_WIDTH        *math.cos(beta2), y2 + side* TRACK_WIDTH        *math.sin(beta2))
                  b2_r = (x2 + side*(TRACK_WIDTH+BORDER)*math.cos(beta2), y2 + side*(TRACK_WIDTH+BORDER)*math.sin(beta2))
                  self.road_poly.append(( [b1_l, b1_r, b2_r, b2_l], (1,1,1) if i%2==0 else (1,0,0) ))
          self.track = track
          return True
  
      def reset(self):
          self._destroy()
          self.reward = 0.0
          self.prev_reward = 0.0
          self.tile_visited_count = 0
          self.t = 0.0
          self.road_poly = []
          self.froad_poly = []
          self.step_cnt = 0
  
          # Color modification.
          self.road_color = np.array([0.4, 0.4, 0.4])  # Original road color.
          self.grass_color = np.array([0.4, 0.8, 0.4, 1])  # Original grass color.
          if self._modification_type == 'color':
              noise1 = np.random.uniform(-0.2, 0.2)
              noise2 = np.random.uniform(-0.2, 0.2)
              print('noise1={}'.format(noise1))
              print('noise2={}'.format(noise2))
              self.road_color += noise1
              self.grass_color[:3] += noise2
  
          while True:
              success = self._create_track()
              if success:
                  break
              if self.verbose == 1:
                  print("retry to generate track (normal if there are not many of this messages)")
          add_blob = self._modification_type == 'blob'
          self.car = Car(self.world, *self.track[0][1:4], add_blob=add_blob)
  
          return self.step(None)[0]
  
      def step(self, action):
          if action is not None:
              self.car.steer(-action[0])
              self.car.gas(action[1])
              self.car.brake(action[2])
  
          self.car.step(1.0/FPS)
          self.world.Step(1.0/FPS, 6*30, 2*30)
          self.t += 1.0/FPS
  
          self.state = self.render("state_pixels")
  
          step_reward = 0
          done = False
          if action is not None: # First step without action, called from reset()
              self.reward -= 0.1
              # We actually don't want to count fuel spent, we want car to be faster.
              # self.reward -=  10 * self.car.fuel_spent / ENGINE_POWER
              self.car.fuel_spent = 0.0
              step_reward = self.reward - self.prev_reward
              self.prev_reward = self.reward
              if self.tile_visited_count==len(self.track):
                  done = True
              x, y = self.car.hull.position
              if abs(x) > PLAYFIELD or abs(y) > PLAYFIELD:
                  done = True
                  step_reward = -100
  
          return self.state, step_reward, done, {}
  
      def render(self, mode='human'):
          assert mode in ['human', 'state_pixels', 'rgb_array']
          if self.viewer is None:
              from gym.envs.classic_control import rendering
              self.viewer = rendering.Viewer(WINDOW_W, WINDOW_H)
              self.score_label = pyglet.text.Label('0000', font_size=36,
                  x=20, y=WINDOW_H*2.5/40.00, anchor_x='left', anchor_y='center',
                  color=(255,255,255,255))
              self.transform = rendering.Transform()
  
          if "t" not in self.__dict__: return  # reset() not called yet
  
          zoom = 0.1*SCALE*max(1-self.t, 0) + ZOOM*SCALE*min(self.t, 1)   # Animate zoom first second
          zoom_state  = ZOOM*SCALE*STATE_W/WINDOW_W
          zoom_video  = ZOOM*SCALE*VIDEO_W/WINDOW_W
          scroll_x = self.car.hull.position[0]
          scroll_y = self.car.hull.position[1]
          angle = -self.car.hull.angle
          vel = self.car.hull.linearVelocity
          if np.linalg.norm(vel) > 0.5:
              angle = math.atan2(vel[0], vel[1])
          self.transform.set_scale(zoom, zoom)
          self.transform.set_translation(
              WINDOW_W/2 - (scroll_x*zoom*math.cos(angle) - scroll_y*zoom*math.sin(angle)),
              WINDOW_H/4 - (scroll_x*zoom*math.sin(angle) + scroll_y*zoom*math.cos(angle)) )
          self.transform.set_rotation(angle)
  
          self.car.draw(self.viewer, mode!="state_pixels")
  
          arr = None
          win = self.viewer.window
          win.switch_to()
          win.dispatch_events()
  
          win.clear()
          t = self.transform
          if mode=='rgb_array':
              VP_W = VIDEO_W
              VP_H = VIDEO_H
          elif mode == 'state_pixels':
              VP_W = STATE_W
              VP_H = STATE_H
          else:
              pixel_scale = 1
              if hasattr(win.context, '_nscontext'):
                  pixel_scale = win.context._nscontext.view().backingScaleFactor()  # pylint: disable=protected-access
              VP_W = int(pixel_scale * WINDOW_W)
              VP_H = int(pixel_scale * WINDOW_H)
  
          gl.glViewport(0, 0, VP_W, VP_H)
          t.enable()
          self.render_road()
          for geom in self.viewer.onetime_geoms:
              geom.render()
          self.viewer.onetime_geoms = []
          t.disable()
          self.render_indicators(WINDOW_W, WINDOW_H)
  
  #        # YUJIN
  #        image_data = pyglet.image.get_buffer_manager().get_color_buffer().get_image_data()
  #        arr = np.fromstring(image_data.data, dtype=np.uint8, sep='')
  #        arr = arr.reshape(VP_H, VP_W, 4)
  #        arr[:(VP_H // 2), :, :3] = 0
  #        image_data = arr.data.__str__()
  #        pyglet.image.ImageData(VP_H, VP_W, 'RGBA', image_data, pitch=VP_W*4)
  
          if mode == 'human':
              win.flip()
              return self.viewer.isopen
  
          image_data = pyglet.image.get_buffer_manager().get_color_buffer().get_image_data()
          arr = np.fromstring(image_data.data, dtype=np.uint8, sep='')
          arr = arr.reshape(VP_H, VP_W, 4)
          arr = arr[::-1, :, 0:3]
  
          return arr
          # YUJIN
          if VP_W == 96:
              self.step_cnt += 1
          if self.step_cnt > 1:
              import os
              import cv2
  #            img_path = os.path.join(
  #                '/usr/local/google/home/yujintang/PycharmProjects/evolution_strategy/models/kof/images')
  #            img_path = os.path.join(
  #                img_path, 'out{}.png'.format(self.step_cnt + 1900 - 200))
  #            img_data = cv2.imread(img_path, cv2.IMREAD_COLOR)
  #            img_data = cv2.resize(img_data, (VP_W, VP_H))
  #            if VP_H == 96:
  #                img_data = cv2.resize(img_data, (144, 96))
  #            h, w, c = img_data.shape
  #            img_data = img_data[:, (w//2 - VP_W//2):(w//2 + VP_W//2), :]
              mask = np.zeros([VP_H, VP_W], dtype=bool)
              mask[(arr[:, :, 1] == 204) | (arr[:, :, 1] == 230)] = True
              rand_noise = np.random.randint(0, 256, VP_W * VP_H * 3)
              rand_noise = rand_noise.reshape([VP_H, VP_W, 3])
              for i in range(3):
  #                arr[:, :, i][mask] = img_data[:, :, 2-i][mask]
                   arr[:, :, i][mask] = rand_noise[:, :, i][mask]
              self.step_cnt += 1
  
          return arr
  
      def close(self):
          if self.viewer is not None:
              self.viewer.close()
              self.viewer = None
  
      def render_road(self):
          gl.glBegin(gl.GL_QUADS)
          gl.glColor4f(*self.grass_color)
          gl.glVertex3f(-PLAYFIELD, +PLAYFIELD, 0)
          gl.glVertex3f(+PLAYFIELD, +PLAYFIELD, 0)
          gl.glVertex3f(+PLAYFIELD, -PLAYFIELD, 0)
          gl.glVertex3f(-PLAYFIELD, -PLAYFIELD, 0)
          gl.glColor4f(0.4, 0.9, 0.4, 1.0)
          k = PLAYFIELD/20.0
          for x in range(-20, 20, 2):
              for y in range(-20, 20, 2):
                  gl.glVertex3f(k*x + k, k*y + 0, 0)
                  gl.glVertex3f(k*x + 0, k*y + 0, 0)
                  gl.glVertex3f(k*x + 0, k*y + k, 0)
                  gl.glVertex3f(k*x + k, k*y + k, 0)
          for poly, color in self.road_poly:
              gl.glColor4f(color[0], color[1], color[2], 1)
              for p in poly:
                  gl.glVertex3f(p[0], p[1], 0)
  
          # YUJIN
          for poly, color in self.froad_poly:
              gl.glColor4f(color[0], color[1], color[2], 1)
              for p in poly:
                  gl.glVertex3f(p[0], p[1], 0)
  
          gl.glEnd()
  
      def render_indicators(self, W, H):
          gl.glBegin(gl.GL_QUADS)
          s = W/40.0
          h = H/40.0
          gl.glColor4f(0,0,0,1)
          gl.glVertex3f(W, 0, 0)
          gl.glVertex3f(W, 5*h, 0)
          gl.glVertex3f(0, 5*h, 0)
          gl.glVertex3f(0, 0, 0)
  
          if self._modification_type == 'bar':
              gl.glVertex3f(W, 5*h, 0)
              gl.glVertex3f(W, H, 0)
              gl.glVertex3f(W-3*s, H, 0)
              gl.glVertex3f(W-3*s, 5*h, 0)
  
              gl.glVertex3f(3*s, 5*h, 0)
              gl.glVertex3f(3*s, H, 0)
              gl.glVertex3f(0, H, 0)
              gl.glVertex3f(0, 5*h, 0)
  
          def vertical_ind(place, val, color):
              gl.glColor4f(color[0], color[1], color[2], 1)
              gl.glVertex3f((place+0)*s, h + h*val, 0)
              gl.glVertex3f((place+1)*s, h + h*val, 0)
              gl.glVertex3f((place+1)*s, h, 0)
              gl.glVertex3f((place+0)*s, h, 0)
          def horiz_ind(place, val, color):
              gl.glColor4f(color[0], color[1], color[2], 1)
              gl.glVertex3f((place+0)*s, 4*h , 0)
              gl.glVertex3f((place+val)*s, 4*h, 0)
              gl.glVertex3f((place+val)*s, 2*h, 0)
              gl.glVertex3f((place+0)*s, 2*h, 0)
          true_speed = np.sqrt(np.square(self.car.hull.linearVelocity[0]) + np.square(self.car.hull.linearVelocity[1]))
          vertical_ind(5, 0.02*true_speed, (1,1,1))
          vertical_ind(7, 0.01*self.car.wheels[0].omega, (0.0,0,1)) # ABS sensors
          vertical_ind(8, 0.01*self.car.wheels[1].omega, (0.0,0,1))
          vertical_ind(9, 0.01*self.car.wheels[2].omega, (0.2,0,1))
          vertical_ind(10,0.01*self.car.wheels[3].omega, (0.2,0,1))
          horiz_ind(20, -10.0*self.car.wheels[0].joint.angle, (0,1,0))
          horiz_ind(30, -0.8*self.car.hull.angularVelocity, (1,0,0))
          gl.glEnd()
          self.score_label.text = "%04i" % self.reward
          self.score_label.draw()
  
  
  if __name__=="__main__":
      from pyglet.window import key
      a = np.array( [0.0, 0.0, 0.0] )
      def key_press(k, mod):
          global restart
          if k==0xff0d: restart = True
          if k==key.LEFT:  a[0] = -1.0
          if k==key.RIGHT: a[0] = +1.0
          if k==key.UP:    a[1] = +1.0
          if k==key.DOWN:  a[2] = +0.8   # set 1.0 for wheels to block to zero rotation
      def key_release(k, mod):
          if k==key.LEFT  and a[0]==-1.0: a[0] = 0
          if k==key.RIGHT and a[0]==+1.0: a[0] = 0
          if k==key.UP:    a[1] = 0
          if k==key.DOWN:  a[2] = 0
      env = CarRacing()
      env.render()
      env.viewer.window.on_key_press = key_press
      env.viewer.window.on_key_release = key_release
      record_video = False
      if record_video:
          from gym.wrappers.monitor import Monitor
          env = Monitor(env, '/tmp/video-test', force=True)
      isopen = True
      while isopen:
          env.reset()
          total_reward = 0.0
          steps = 0
          restart = False
          while True:
              s, r, done, info = env.step(a)
              total_reward += r
              if steps % 200 == 0 or done:
                  print("\naction " + str(["{:+0.2f}".format(x) for x in a]))
                  print("step {} total_reward {:+0.2f}".format(steps, total_reward))
                  #import matplotlib.pyplot as plt
                  #plt.imshow(s)
                  #plt.savefig("test.jpeg")
              steps += 1
              isopen = env.render()
              if done or restart or isopen == False:
                  break
      env.close()
  