def __init__(self):
self._ale = ALEInterface()
self._ale.setInt('random_seed', 123)
self._ale.setFloat('repeat_action_probability', 0.0)
self._ale.setBool('color_averaging', False)
self._ale.loadROM('roms/enduro.bin')
self._controller = Controller(self._ale)
self._extractor = StateExtractor(self._ale)
self._image = None
def __init__(self):
self._ale = ALEInterface()
self._ale.setInt('random_seed', 123)
self._ale.setFloat('repeat_action_probability', 0.0)
self._ale.setBool('color_averaging', False)
self._ale.loadROM('roms/enduro.bin')
self._controller = Controller(self._ale)
self._extractor = StateExtractor(self._ale)
self._image = None
class Agent(object):
def __init__(self):
self._ale = ALEInterface()
self._ale.setInt('random_seed', 123)
self._ale.setFloat('repeat_action_probability', 0.0)
self._ale.setBool('color_averaging', False)
self._ale.loadROM('roms/enduro.bin')
self._controller = Controller(self._ale)
self._extractor = StateExtractor(self._ale)
self._image = None
def run(self, learn, episodes=1, draw=False):
""" Implements the playing/learning loop.
Args:
learn(bool): Whether the self.learn() function should be called.
episodes (int): The number of episodes to run the agent for.
draw (bool): Whether to overlay the environment state on the frame.
Returns:
None
"""
for e in range(episodes):
# Observe the environment to set the initial state
(grid, self._image) = self._extractor.run(draw=draw, scale=4.0)
self.initialise(grid)
num_frames = self._ale.getFrameNumber()
# Each episode lasts 6500 frames
while self._ale.getFrameNumber() - num_frames < 6500:
# Take an action
self.act()
# Update the environment grid
(grid, self._image) = self._extractor.run(draw=draw, scale=4.0)
self.sense(grid)
# Perform learning if required
if learn:
self.learn()
self.callback(learn, e + 1,
self._ale.getFrameNumber() - num_frames)
self._ale.reset_game()
def getActionsSet(self):
""" Returns the set of all possible actions
"""
return [Action.ACCELERATE, Action.RIGHT, Action.LEFT, Action.BRAKE]
def move(self, action):
""" Executes the action and advances the game to the next state.
Args:
action (int): The action which should executed. Make sure to use
the constants returned by self.getActionsSet()
Returns:
int: The obtained reward after executing the action
"""
return self._controller.move(action)
def initialise(self, grid):
""" Called at the beginning of each episode, mainly used
for state initialisation.
Args:
grid (np.ndarray): 11x10 array with the initial environment grid.
Returns:
None
"""
raise NotImplementedError
def act(self):
""" Called at each loop iteration to choose and execute an action.
Returns:
None
"""
raise NotImplementedError
def sense(self, grid):
""" Called at each loop iteration to construct the new state from
the update environment grid.
Returns:
None
"""
raise NotImplementedError
def learn(self):
""" Called at each loop iteration when the agent is learning. It should
implement the learning procedure.
Returns:
None
"""
raise NotImplementedError
def callback(self, learn, episode, iteration):
""" Called at each loop iteration mainly for reporting purposes.
Args:
learn (bool): Indicates whether the agent is learning or not.
episode (int): The number of the current episode.
iteration (int): The number of the current iteration.
Returns:
None
"""
raise NotImplementedError
class Agent(object):
def __init__(self):
self._ale = ALEInterface()
self._ale.setInt('random_seed', 123)
self._ale.setFloat('repeat_action_probability', 0.0)
self._ale.setBool('color_averaging', False)
self._ale.loadROM('roms/enduro.bin')
self._controller = Controller(self._ale)
self._extractor = StateExtractor(self._ale)
self._image = None
def run(self, learn, episodes=1, draw=False):
""" Implements the playing/learning loop.
Args:
learn(bool): Whether the self.learn() function should be called.
episodes (int): The number of episodes to run the agent for.
draw (bool): Whether to overlay the environment state on the frame.
Returns:
None
"""
for e in range(episodes):
# Observe the environment to set the initial state
(grid, self._image) = self._extractor.run(draw=draw, scale=4.0)
self.initialise(grid)
num_frames = self._ale.getFrameNumber()
# Each episode lasts 6500 frames
while self._ale.getFrameNumber() - num_frames < 6500:
# Take an action
self.act()
# Update the environment grid
(grid, self._image) = self._extractor.run(draw=draw, scale=4.0)
self.sense(grid)
# Perform learning if required
if learn:
self.learn()
self.callback(learn, e + 1, self._ale.getFrameNumber() - num_frames)
self._ale.reset_game()
def getActionsSet(self):
""" Returns the set of all possible actions
"""
return [Action.ACCELERATE, Action.RIGHT, Action.LEFT, Action.BREAK]
def move(self, action):
""" Executes the action and advances the game to the next state.
Args:
action (int): The action which should executed. Make sure to use
the constants returned by self.getActionsSet()
Returns:
int: The obtained reward after executing the action
"""
return self._controller.move(action)
def initialise(self, grid):
""" Called at the beginning of each episode, mainly used
for state initialisation.
Args:
grid (np.ndarray): 11x10 array with the initial environment grid.
Returns:
None
"""
raise NotImplementedError
def act(self):
""" Called at each loop iteration to choose and execute an action.
Returns:
None
"""
raise NotImplementedError
def sense(self, grid):
""" Called at each loop iteration to construct the new state from
the update environment grid.
Returns:
None
"""
raise NotImplementedError
def learn(self):
""" Called at each loop iteration when the agent is learning. It should
implement the learning procedure.
Returns:
None
"""
raise NotImplementedError
def callback(self, learn, episode, iteration):
""" Called at each loop iteration mainly for reporting purposes.
Args:
learn (bool): Indicates whether the agent is learning or not.
episode (int): The number of the current episode.
iteration (int): The number of the current iteration.
Returns:
None
"""
raise NotImplementedError
class Agent(object):
def __init__(self):
self._ale = ALEInterface()
self._ale.setInt('random_seed', 123)
self._ale.setFloat('repeat_action_probability', 0.0)
self._ale.setBool('color_averaging', False)
self._ale.loadROM('roms/enduro.bin')
self._controller = Controller(self._ale)
self._extractor = StateExtractor(self._ale)
self._image = None
self._speed_range = 50
def run(self, learn, episodes=1, draw=False):
""" Implements the playing/learning loop.
Args:
learn(bool): Whether the self.learn() function should be called.
episodes (int): The number of episodes to run the agent for.
draw (bool): Whether to overlay the environment state on the frame.
Returns:
None
"""
for e in range(episodes):
self._relative_speed = -self._speed_range
# Observe the environment to set the initial state
(road, cars, grid, self._image) = self._extractor.run(draw=draw,
scale=4.0)
self.initialise(road, cars, self._relative_speed, grid)
num_frames = self._ale.getFrameNumber()
# Each episode lasts 6500 frames
while self._ale.getFrameNumber() - num_frames < 6500:
# Take an action
self.act()
# Update the environment grid
(road, cars, grid,
self._image) = self._extractor.run(draw=draw, scale=4.0)
if self.collision(cars):
self._relative_speed = -self._speed_range
self.sense(road, cars, self._relative_speed, grid)
# Perform learning if required
if learn:
self.learn()
self.callback(learn, e + 1,
self._ale.getFrameNumber() - num_frames)
self._ale.reset_game()
def collision(self, cars):
if not cars['others']:
return False
x, y, _, _ = cars['self']
min_dist = sys.float_info.max
min_angle = 0.
for c in cars['others']:
cx, cy, _, _ = c
dist = np.sqrt((cx - x)**2 + (cy - y)**2)
if dist < min_dist:
min_dist = dist
min_angle = np.arctan2(y - cy, cx - x)
return min_dist < 18. and 0.1 * np.pi < min_angle and min_angle < 0.9 * np.pi
def getActionsSet(self):
""" Returns the set of all possible actions
"""
return [Action.ACCELERATE, Action.RIGHT, Action.LEFT, Action.BRAKE]
def move(self, action):
""" Executes the action and advances the game to the next state.
Args:
action (int): The action which should executed. Make sure to use
the constants returned by self.getActionsSet()
Returns:
int: The obtained reward after executing the action
"""
if action == Action.ACCELERATE:
self._relative_speed = min(self._relative_speed + 1,
self._speed_range)
elif action == Action.BRAKE:
self._relative_speed = max(self._relative_speed - 1,
-self._speed_range)
return self._controller.move(action)
def initialise(self, road, cars, speed, grid):
""" Called at the beginning of each episode, mainly used
for state initialisation. For more information on the arguments
have a look at the README.md
Args:
road: 2-dimensional array containing [x, y] points
in pixel coordinates of the road grid
cars: dictionary which contains the location and the size
of the agent and the opponents in pixel coordinates
speed: the relative speed of the agent with respect the others
gird: 2-dimensional numpy array containing the latest grid
representation of the environment
Returns:
None
"""
raise NotImplementedError
def act(self):
""" Called at each loop iteration to choose and execute an action.
Returns:
None
"""
raise NotImplementedError
def sense(self, road, cars, speed, grid):
""" Called at each loop iteration to construct the new state from
the update environment grid. For more information on the arguments
have a look at the README.md
Args:
road: 2-dimensional array containing [x, y] points
in pixel coordinates of the road grid
cars: dictionary which contains the location and the size
of the agent and the opponents in pixel coordinates
speed: the relative speed of the agent with respect the others
gird: 2-dimensional numpy array containing the latest grid
representation of the environment
Returns:
None
"""
raise NotImplementedError
def learn(self):
""" Called at each loop iteration when the agent is learning. It should
implement the learning procedure.
Returns:
None
"""
raise NotImplementedError
def callback(self, learn, episode, iteration):
""" Called at each loop iteration mainly for reporting purposes.
Args:
learn (bool): Indicates whether the agent is learning or not.
episode (int): The number of the current episode.
iteration (int): The number of the current iteration.
Returns:
None
"""
raise NotImplementedError