def __init__()

in agents/ed_pcq.py [0:0]

• 48 lines of code
• 1 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,
                 num_qs=50,
                 num_q_layers=3,
                 q_eta=1.0,
                 ):

        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.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.num_qs = num_qs
        self.q_eta = q_eta
        self.critic = ParallelizedEnsembleFlattenMLP(ensemble_size=num_qs,
                                                     hidden_sizes=[256] * num_q_layers,
                                                     input_size=state_dim + action_dim,
                                                     output_size=1,
                                                     layer_norm=None,
                                                     ).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