class DonkeyEnv(gym.Env):
"""
OpenAI Gym Environment for Donkey
"""
metadata = {
"render.modes": ["human", "rgb_array"],
}
ACTION_NAMES = ["steer", "throttle"]
STEER_LIMIT_LEFT = -1.0
STEER_LIMIT_RIGHT = 1.0
THROTTLE_MIN = 0.0
THROTTLE_MAX = 1.0
VAL_PER_PIXEL = 255
def __init__(self, level, conf=None):
print("starting DonkeyGym env")
self.viewer = None
self.proc = None
if conf is None:
conf = {}
conf["level"] = level
# ensure defaults are supplied if missing.
supply_defaults(conf)
# set logging level
logging.basicConfig(level=conf["log_level"]) # pytype: disable=key-error
logger.debug("DEBUG ON")
logger.debug(conf)
# start Unity simulation subprocess
self.proc = None
if "exe_path" in conf:
self.proc = DonkeyUnityProcess()
# the unity sim server will bind to the host ip given
self.proc.start(conf["exe_path"],
host="0.0.0.0",
port=conf["port"])
# wait for simulator to startup and begin listening
time.sleep(conf["start_delay"])
# start simulation com
self.viewer = DonkeyUnitySimContoller(conf=conf)
# steering and throttle
self.action_space = spaces.Box(
low=np.array([self.STEER_LIMIT_LEFT, self.THROTTLE_MIN]),
high=np.array([self.STEER_LIMIT_RIGHT, self.THROTTLE_MAX]),
dtype=np.float64, # debug
)
# camera sensor data
self.observation_space = spaces.Box(0,
self.VAL_PER_PIXEL,
self.viewer.get_sensor_size(),
dtype=np.uint8)
# simulation related variables.
self.seed()
# Frame Skipping
self.frame_skip = conf["frame_skip"] # pytype: disable=key-error
# wait until loaded
self.viewer.wait_until_loaded()
def __del__(self):
self.close()
def close(self):
if hasattr(self, "viewer") and self.viewer is not None:
self.viewer.quit()
if hasattr(self, "proc") and self.proc is not None:
self.proc.quit()
def set_reward_fn(self, reward_fn):
self.viewer.set_reward_fn(reward_fn)
def set_episode_over_fn(self, ep_over_fn):
self.viewer.set_episode_over_fn(ep_over_fn)
def seed(self, seed=None):
self.np_random, seed = seeding.np_random(seed)
return [seed]
def step(self, action):
for i in range(self.frame_skip):
self.viewer.take_action(action)
observation, reward, done, info = self.viewer.observe()
return observation, reward, done, info
def reset(self):
self.viewer.reset()
observation, reward, done, info = self.viewer.observe()
# time.sleep(1) # make it faster
return observation
def render(self, mode="human", close=False):
# def render(self, mode="rgb_array", close=False):
if close:
self.viewer.quit()
return self.viewer.render(mode)
def is_game_over(self):
return self.viewer.is_game_over()
class DonkeyEnv(gym.Env):
"""
OpenAI Gym Environment for Donkey
"""
metadata = {
"render.modes": ["human", "rgb_array"],
}
ACTION_NAMES = ["steer", "throttle"]
STEER_LIMIT_LEFT = -1.0
STEER_LIMIT_RIGHT = 1.0
THROTTLE_MIN = 0.0
THROTTLE_MAX = 5.0
VAL_PER_PIXEL = 255
def __init__(self, level=0, exe_path="self_start", host='127.0.0.1', port=9091, frame_skip=2, start_delay=5.0, cam_resolution=(120,160,3)):
print("starting DonkeyGym env")
self.viewer = None
self.proc = None
# start Unity simulation subprocess
self.proc = DonkeyUnityProcess()
# the unity sim server will bind to the host ip given
self.proc.start(exe_path, host='0.0.0.0', port=port)
# wait for simulator to startup and begin listening
time.sleep(start_delay)
# start simulation com
self.viewer = DonkeyUnitySimContoller(level=level, host=host, port=port, cam_resolution=cam_resolution)
# steering and throttle
self.action_space = spaces.Box(low=np.array([self.STEER_LIMIT_LEFT, self.THROTTLE_MIN]),
high=np.array([self.STEER_LIMIT_RIGHT, self.THROTTLE_MAX]), dtype=np.float32)
# camera sensor data
self.observation_space = spaces.Box(
0, self.VAL_PER_PIXEL, self.viewer.get_sensor_size(), dtype=np.uint8)
# simulation related variables.
self.seed()
# Frame Skipping
self.frame_skip = frame_skip
# wait until loaded
self.viewer.wait_until_loaded()
# send car config
# self.viewer.set_car_config("car01", (255, 0, 0), "Tawn", 100)
def __del__(self):
self.close()
def close(self):
if self.viewer is not None:
self.viewer.quit()
if self.proc is not None:
self.proc.quit()
def set_reward_fn(self, reward_fn):
self.viewer.set_reward_fn(reward_fn)
def set_episode_over_fn(self, ep_over_fn):
self.viewer.set_episode_over_fn(ep_over_fn)
def seed(self, seed=None):
self.np_random, seed = seeding.np_random(seed)
return [seed]
def step(self, action):
for i in range(self.frame_skip):
self.viewer.take_action(action)
observation, reward, done, info = self.viewer.observe()
return observation, reward, done, info
def reset(self):
self.viewer.reset()
observation, reward, done, info = self.viewer.observe()
time.sleep(1)
return observation
def render(self, mode="human", close=False):
if close:
self.viewer.quit()
return self.viewer.render(mode)
def is_game_over(self):
return self.viewer.is_game_over()