def _make_ml_execute(self, scene_info):
"""Send the scene_info to the ml process and wait for the instruction
"""
comm.send_to_ml(scene_info, self._ml_name)
time.sleep(self._ml_execute_time)
instruction = self._instruct_receiver.recv(self._ml_name)
if instruction.frame != -1 and \
scene_info.frame - instruction.frame > self._frame_delayed:
self._frame_delayed = scene_info.frame - instruction.frame
print("Delayed {} frame(s)".format(self._frame_delayed))
return instruction
def _make_ml_execute(self, scene_info):
"""
Send the scene_info to the ml process and wait for the instruction
"""
comm.send_to_ml(scene_info, self._ml_name)
time.sleep(self._ml_execute_time)
game_cmd = self._process_cmd(comm.recv_from_ml(self._ml_name))
if (game_cmd["frame"] != -1 and
scene_info["frame"] - game_cmd["frame"] > self._frame_delayed):
self._frame_delayed = scene_info["frame"] - game_cmd["frame"]
print("Delayed {} frame(s)".format(self._frame_delayed))
return game_cmd["command"]
def game_loop(self):
"""
The main loop of the game in machine learning mode
The execution order in the loop:
1. Send the scene information to the machine learning process.
2. Wait for the key frame (During this period, machine learning process can
process the information and generate the game instruction.)
3. Check if there has a command to receive. If it has, receive the
command. Otherwise, generate a dummy one.
4. Pass the command received to the game and update the scene.
5. If the game is over or passed, send the scene information containing the
game status to the machine learning process, and reset the game.
6. Back to 1.
"""
comm.wait_ml_ready(self._ml_name)
while not quit_or_esc():
scene_info = self._scene.get_scene_info()
command = self._make_ml_execute(scene_info)
scene_info["command"] = command.value
self._record_handler(scene_info)
game_status = self._scene.update(command)
self._screen.update(self._scene.catch_ball_times)
if (game_status == GameStatus.GAME_OVER
or game_status == GameStatus.GAME_PASS):
scene_info = self._scene.get_scene_info()
comm.send_to_ml(scene_info, self._ml_name)
scene_info["command"] = None
self._record_handler(scene_info)
print(game_status.value)
if self._one_shot_mode:
return
self._scene.reset()
self._frame_delayed = 0
# Wait for ml process doing resetting jobs
comm.wait_ml_ready(self._ml_name)
def game_loop(self):
"""
The game execution loop
"""
# Wait for the ml process
comm.wait_ml_ready(self._ml_name)
while not quit_or_esc():
# Generate the scene information
scene_info = self._scene.get_scene_info()
# Send the scene information to the ml process
# and wait the command sent from it
command = self._make_ml_execute(scene_info)
# Record the scene information
scene_info["command"] = command.value
self._record_handler(scene_info)
# Update the scene
game_status = self._scene.update(command)
# If the game is over, reset the scene or
# quit the game loop if one shot mode is set.
if game_status == GameStatus.GAME_OVER:
# Send the scene info with the game over status
# and record that scene info
scene_info = self._scene.get_scene_info()
comm.send_to_ml(scene_info, self._ml_name)
scene_info["command"] = None
self._record_handler(scene_info)
if self._one_shot_mode:
return
self._scene.reset()
self._frame_delayed = 0
# Wait for the ml process for the next round
comm.wait_ml_ready(self._ml_name)
# Draw the scene to the display
self._draw_scene()
def _make_ml_execute(self, scene_info):
"""
Send the scene info to the ml process and receive the command from it
The method will wait the ml process to execute the command for a period
which is decided by the `_ml_execution_time`.
If the ml process can't send the command in time,
the method will return a default command.
"""
comm.send_to_ml(scene_info, self._ml_name)
time.sleep(self._ml_execution_time)
game_cmd = self._cmd_receiver.recv(self._ml_name)
# Check and update the frame delayed
if (game_cmd.frame != -1 and
scene_info.frame - game_cmd.frame > self._frame_delayed):
self._frame_delayed = scene_info.frame - game_cmd.frame
return game_cmd.command
def game_loop(self):
"""The main loop of the game in machine learning mode
The execution order in the loop:
1. Send the SceneInfo to the machine learning process.
2. Wait for the key frame (During this period, machine learning process can
process the SceneInfo and generate the GameInstruction.)
3. Check if there has a GameInstruction to receive. If it has, receive the
instruction. Otherwise, generate a dummy one.
4. Pass the GameInstruction to the game and update the scene (and frame no.)
5. If the game is over or passed, send the SceneInfo containing the
game status to the machine learning process, and reset the game.
6. Back to 1.
"""
keep_going = lambda: not quit_or_esc()
comm.wait_ml_ready(self._ml_name)
while keep_going():
scene_info = self._scene.fill_scene_info_obj(SceneInfo())
self._record_handler(scene_info)
instruction = self._make_ml_execute(scene_info)
game_status = self._scene.update(instruction.command)
self._draw_scene()
if game_status == GameStatus.GAME_OVER or \
game_status == GameStatus.GAME_PASS:
scene_info = self._scene.fill_scene_info_obj(SceneInfo())
self._record_handler(scene_info)
comm.send_to_ml(scene_info, self._ml_name)
if self._one_shot_mode:
return
self._scene.reset()
self._frame_delayed = 0
# Wait for ml process doing resetting jobs
comm.wait_ml_ready(self._ml_name)
pygame.quit()
