def __init__(self, name):
FlappyAgent.__init__(self, name)
self.player_y_bins = np.linspace(0, 512, 15)
self.player_vel_bins = np.linspace(-8, 10, 15)
self.next_pipe_dist_bins = np.linspace(0, 288, 15)
self.next_pipe_top_bins = np.linspace(0, 512, 15)
def __init__(self, name):
FlappyAgent.__init__(self, name)
self.lr = 0.00001
self.episode = []
self.weights = [
np.array([0] * 4, dtype=np.float64),
np.array([0] * 4, dtype=np.float64)
] # [0 flap, 0 noop]
def __init__(self, name):
FlappyAgent.__init__(self, name)
self.player_pipe_difference_bins = np.linspace(13, 76, 3)
def __init__(self, name):
FlappyAgent.__init__(self, name)
self.learning_rate = 0.00001
self.episode = []
self.weights = [np.array([0]*4, dtype=np.float64), np.array([0]*4, dtype=np.float64)]
self.epoch_iteration += 1
self.epoch_reward_sum += reward
if done:
print('\nAggregate test reward:', self.epoch_reward_sum)
self.state = self.env.reset()
self.epoch_reward_sum = 0
self.epoch_iteration = 0
self.draw()
call_latency = self.test_update_interval
self.window.after(call_latency, self.loop)
if __name__ == '__main__':
env_scaler = 10
env = Environment()
n_iterations = int(2e5)
agent = FlappyAgent(observation_space_size=env.observation_space_size,
action_space=env.action_space,
n_iterations=n_iterations)
gui = FlappyGUI(agent=agent,
n_iterations=n_iterations,
env=env,
env_scaler=10,
train_update_interval=25,
test_update_interval=50)
def __init__(self, name):
FlappyAgent.__init__(self, name)
self.player_pipe_difference_bins = np.linspace(13, 76, 11)
self.pipe_next_pipe_difference_bins = np.linspace(-158, 158, 5)
def __init__(self, name):
FlappyAgent.__init__(self, name)
self.pipe_next_pipe_difference_bins = np.linspace(-158, 158, 5)
self.distance_to_pipe_bins = np.linspace(0, 65, 4)
self.bird_pipe_difference_bins = np.linspace(0, 100, 11)
def __init__(self, name):
FlappyAgent.__init__(self, name)
self.count = 0
self.split = 5
# For plotting metrics
epoch_iterations_list = []
epoch_rewards_list = []
best_epoch_reward_sum = -1
best_epoch = 0
n_iterations = int(2e5)
iteration = 0
epoch = 0
print_epoch_interval = 500
env = Environment()
agent = FlappyAgent(observation_space_size=env.observation_space_size,
action_space=env.action_space,
n_iterations=n_iterations)
while iteration < n_iterations:
state = env.reset()
epoch_iteration, epoch_reward_sum = 0, 0
done = False
while not done:
action = agent.step(state)
next_state, reward, done, info = env.step(action)
agent.learn(state, action, next_state, reward)
state = next_state