def __init__()

in agents/ql_diffusion.py [0:0]

• 45 lines of code
• 2 McCabe index (conditional complexity)

    def __init__(self,
                 state_dim,
                 action_dim,
                 max_action,
                 device,
                 discount,
                 tau,
                 max_q_backup=False,
                 eta=0.1,
                 model_type='MLP',
                 beta_schedule='linear',
                 n_timesteps=100,
                 ema_decay=0.995,
                 step_start_ema=1000,
                 update_ema_every=5,
                 lr=3e-4,
                 lr_decay=False,
                 lr_maxt=int(1e6),
                 mode='whole_grad',
                 ):

        self.model = MLP(state_dim=state_dim, action_dim=action_dim, device=device)

        self.actor = Diffusion(state_dim=state_dim, action_dim=action_dim, model=self.model, max_action=max_action,
                               beta_schedule=beta_schedule, n_timesteps=n_timesteps,
                               ).to(device)
        self.actor_optimizer = torch.optim.Adam(self.actor.parameters(), lr=lr)

        self.lr_decay = lr_decay
        if lr_decay:
            self.actor_lr_scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(self.actor_optimizer, T_max=lr_maxt)

        self.step = 0
        self.step_start_ema = step_start_ema
        self.ema = EMA(ema_decay)
        self.ema_model = copy.deepcopy(self.actor)
        self.update_ema_every = update_ema_every

        self.critic = Critic(state_dim, action_dim).to(device)
        self.critic_target = copy.deepcopy(self.critic)
        self.critic_optimizer = torch.optim.Adam(self.critic.parameters(), lr=lr)

        self.state_dim = state_dim
        self.max_action = max_action
        self.action_dim = action_dim
        self.discount = discount
        self.tau = tau
        self.eta = eta  # q_learning weight
        self.device = device
        self.max_q_backup = max_q_backup

        self.mode = mode