def __init__(self):
self.env = Environment(Shape.Worm())
self.checkpoint_dir = os.path.join(os.path.dirname(__file__), 'mlflow')
self.best_run = 0
self.addative_noise_generator = noise_generators.OUActionNoise(
output_size=self.env.action_size)
self.multiplier_noise_generator = noise_generators.MarkovSaltPepperNoise(
output_size=self.env.action_size)
self.init_models()
self.target_actor.set_weights(self.actor_model.get_weights())
self.target_critic.set_weights(self.critic_model.get_weights())
self.critic_optimizer = tf.keras.optimizers.RMSprop(
learning_rate=self.CRITIC_LR)
self.actor_optimizer = tf.keras.optimizers.RMSprop(
learning_rate=self.ACTOR_LR)
self.buffer = PrioritizedBuffer(self.env.state_size,
self.env.action_size, self.GAMMA,
self.BUFFER_SIZE, self.BATCH_SIZE)
self.episode_reward_history = []
# show controls the appearance of a window with graphics, controlled by 's' and 'a' on the keyboard
self.show = False
# show controls if the model is learning or not, affects the FPS of the graphics
# controlled by 'l' and 'k' on the keyboard
self.learn = True
self.log_params()
