lambda x, y: (x[5] - x[7]) * (1 - y), # bot - playery
lambda x, y: x[6] * y, # velocity
lambda x, y: x[6] * (1 - y),
lambda x, y: x[4] * y, # pipe distance
lambda x, y: x[4] * (1 - y),
# lambda x, y: (x[3] - x[7]) / (x[6] ) * (1 - y) + ,
]
STEPS_PER_EPOCHS = 1000
EPOCHS = 60
EPSILON_START = 0.01
EPSILON_DECAY = EPOCHS * STEPS_PER_EPOCHS
EPSILON_MIN = 0.00000
EPSILON_DECAY_V = (EPSILON_MIN - EPSILON_START) / EPSILON_DECAY
game = flappy.FlappyClone()
env = PLE(game,
display_screen=True,
force_fps=True,
fps=30,
state_preprocessor=preprocessor)
env.init()
approxQAgent = ApproxQAgent(env.getActionSet(),
env.getGameStateDims(),
features,
learningRate=.002)
reward = 0.
epsilon = EPSILON_START
for e in range(EPOCHS):
avgloss = 0.
FREEZE_INTERVAL = 1000
LEARNING_RATE = 0.005
LEARNING_RATE_DECAY = 0.99
MOMENTUM = 0.95
REPLAY_MEMORY_SIZE = 500000
RMS_DECAY = 0.3
RMS_EPSILON = 0.0001
UPDATE_FREQUENCY = 2
UPDATE_RULE = 'rmsprop'
if __name__ == "__main__":
test = False
game = flappy.FlappyClone(black=True)
# game = Catcher(width=64, height=64)
logging.basicConfig(level=logging.INFO)
# --- Parse parameters ---
parameters = process_args(sys.argv[1:], Defaults)
if parameters.deterministic:
rng = np.random.RandomState(523456)
else:
rng = np.random.RandomState()
# --- Instantiate environment ---
env = PLE_env(rng, game=game, frame_skip=parameters.frame_skip,
ple_options={"display_screen": True, "force_fps":not test, "fps":30,
"rng":rng})
if state[next_pipe_dist_to_player] < PIPE_DIST_DELTA:
targetH = selectedPipeBotY - V_MARGIN
else:
targetH = selectedPipeBotY - DELTA_H
if (state[player_y] > targetH):
action = flap
return self.possibleActions[action]
def preprocessor(state):
return np.array([state[k] for k in sorted(state.keys())])
game = flappy.FlappyClone(crazy=False)
env = PLE(game, display_screen=True, force_fps=True, fps=30,
state_preprocessor=preprocessor)
env.init()
expertAgent = ExpertAgent(env.getActionSet(), env.getGameStateDims())
for e in range(1, 101):
while True:
if env.game_over(): # if the game is over, reset
print("test {}, death at score: {}".format(e, game.getScore()))
env.game.tick(1. / 2.)
env.reset_game()
break
reward = env.act(expertAgent.getAction(env.getGameState()))
print("score={:010.1f}".format(game.getScore()), end="\r")
env.game.tick(FPS)
