def __init__(self, task):
    self.task = task
    self.state_size = task.state_size
    self.action_size = task.action_size
    self.action_low = task.action_low
    self.action_high = task.action_high
    self.actor_local = Actor(self.state_size, self.action_size, self.action_low, self.action_high)
    self.actor_target = Actor(self.state_size, self.action_size, self.action_low, self.action_high)
    self.critic_local = Critic(self.state_size, self.action_size)
    self.critic_target = Critic(self.state_size, self.action_size)
    self.critic_target.model.set_weights(self.critic_local.model.get_weights())
    self.actor_target.model.set_weights(self.actor_local.model.get_weights())
    self.exploration_mu = 0
    self.exploration_theta = 0.15
    self.exploration_sigma = 0.2
    self.noise = OUNoise(self.action_size, self.exploration_mu, self.exploration_theta, self.exploration_sigma)
    self.buffer_size = 100000
    self.batch_size = 64
    self.memory = ReplayBuffer(self.buffer_size, self.batch_size)
    self.gamma = 0.99
    self.tau = 0.001
    self.reward_total = 0
    self.best_reward = (- np.Inf)
    self.score = 0
    self.count = 0
def __init__(self, task):
    self.task = task
    self.state_size = task.state_size
    self.action_size = task.action_size
    self.action_low = task.action_low
    self.action_high = task.action_high
    self.actor_local = Actor(self.state_size, self.action_size, self.action_low, self.action_high)
    self.actor_target = Actor(self.state_size, self.action_size, self.action_low, self.action_high)
    self.critic_local = Critic(self.state_size, self.action_size)
    self.critic_target = Critic(self.state_size, self.action_size)
    self.critic_target.model.set_weights(self.critic_local.model.get_weights())
    self.actor_target.model.set_weights(self.actor_local.model.get_weights())
    self.exploration_mu = 0
    self.exploration_theta = 0.15
    self.exploration_sigma = 0.2
    self.noise = OUNoise(self.action_size, self.exploration_mu, self.exploration_theta, self.exploration_sigma)
    self.buffer_size = 100000
    self.batch_size = 64
    self.memory = ReplayBuffer(self.buffer_size, self.batch_size)
    self.gamma = 0.99
    self.tau = 0.001
    self.reward_total = 0
    self.best_reward = (- np.Inf)
    self.score = 0
    self.count = 0