agents/bc_kl.py [135:150]:
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                 ):

        latent_dim = action_dim * 2
        self.vae = VAE(state_dim, action_dim, latent_dim, max_action, device).to(device)
        self.vae_optimizer = torch.optim.Adam(self.vae.parameters(), lr=lr)

        self.actor = RegularActor(state_dim, action_dim, max_action, device).to(device)
        self.actor_optimizer = torch.optim.Adam(self.actor.parameters(), lr=lr)

        self.max_action = max_action
        self.action_dim = action_dim
        self.discount = discount
        self.tau = tau
        self.device = device

        self.num_samples_match = num_samples_match
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agents/bc_mmd.py [145:160]:
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                 ):

        latent_dim = action_dim * 2
        self.vae = VAE(state_dim, action_dim, latent_dim, max_action, device).to(device)
        self.vae_optimizer = torch.optim.Adam(self.vae.parameters(), lr=lr)

        self.actor = RegularActor(state_dim, action_dim, max_action, device).to(device)
        self.actor_optimizer = torch.optim.Adam(self.actor.parameters(), lr=lr)

        self.max_action = max_action
        self.action_dim = action_dim
        self.discount = discount
        self.tau = tau
        self.device = device

        self.num_samples_match = num_samples_match
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