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SAC networks
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| def init_nets(self, global_nets=None): | |
| ''' | |
| Networks: net(actor/policy), q1_net, target_q1_net, q2_net, target_q2_net | |
| All networks are separate, and have the same hidden layer architectures and optim specs, so tuning is minimal | |
| ''' | |
| self.shared = False # SAC does not share networks | |
| NetClass = getattr(net, self.net_spec['type']) | |
| # main actor network | |
| self.net = NetClass(self.net_spec, self.body.state_dim, net_util.get_out_dim(self.body)) | |
| self.net_names = ['net'] | |
| # two critic Q-networks to mitigate positive bias in q_loss and speed up training, uses q_net.py with prefix Q | |
| QNetClass = getattr(net, 'Q' + self.net_spec['type']) | |
| q_in_dim = [self.body.state_dim, self.body.action_dim] | |
| self.q1_net = QNetClass(self.net_spec, q_in_dim, 1) | |
| self.target_q1_net = QNetClass(self.net_spec, q_in_dim, 1) | |
| self.q2_net = QNetClass(self.net_spec, q_in_dim, 1) | |
| self.target_q2_net = QNetClass(self.net_spec, q_in_dim, 1) | |
| self.net_names += ['q1_net', 'target_q1_net', 'q2_net', 'target_q2_net'] | |
| net_util.copy(self.q1_net, self.target_q1_net) | |
| net_util.copy(self.q2_net, self.target_q2_net) | |
| # temperature variable to be learned, and its target entropy | |
| self.log_alpha = torch.zeros(1, requires_grad=True, device=self.net.device) | |
| self.alpha = self.log_alpha.detach().exp() | |
| self.target_entropy = - np.product(self.body.action_space.shape) | |
| # init net optimizer and its lr scheduler | |
| self.optim = net_util.get_optim(self.net, self.net.optim_spec) | |
| self.lr_scheduler = net_util.get_lr_scheduler(self.optim, self.net.lr_scheduler_spec) | |
| self.q1_optim = net_util.get_optim(self.q1_net, self.q1_net.optim_spec) | |
| self.q1_lr_scheduler = net_util.get_lr_scheduler(self.q1_optim, self.q1_net.lr_scheduler_spec) | |
| self.q2_optim = net_util.get_optim(self.q2_net, self.q2_net.optim_spec) | |
| self.q2_lr_scheduler = net_util.get_lr_scheduler(self.q2_optim, self.q2_net.lr_scheduler_spec) | |
| self.alpha_optim = net_util.get_optim(self.log_alpha, self.net.optim_spec) | |
| self.alpha_lr_scheduler = net_util.get_lr_scheduler(self.alpha_optim, self.net.lr_scheduler_spec) | |
| net_util.set_global_nets(self, global_nets) | |
| self.post_init_nets() |
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