in agents/ql_cvae.py [0:0]
def __init__(self,
state_dim,
action_dim,
max_action,
device,
discount,
tau,
max_q_backup=False,
eta=0.1,
ema_decay=0.995,
step_start_ema=1000,
update_ema_every=5,
lr=3e-4,
):
latent_dim = action_dim * 2
self.actor = actor(state_dim, action_dim, latent_dim, max_action, device, hidden_dim=500).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.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.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