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@ -109,10 +109,7 @@ class PixelEncoder(nn.Module):
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out_dim = OUT_DIM[num_layers]
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self.fc = nn.Linear(num_filters * out_dim * out_dim, self.feature_dim * 2)
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self.ln = nn.LayerNorm(self.feature_dim * 2)
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<<<<<<< HEAD
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self.combine = nn.Linear(self.feature_dim + 6, self.feature_dim)
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=======
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>>>>>>> origin/tester_1
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self.outputs = dict()
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@ -157,12 +154,8 @@ class PixelEncoder(nn.Module):
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out = self.reparameterize(mu, logstd)
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self.outputs['tanh'] = out
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<<<<<<< HEAD
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return out, mu, logstd
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=======
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return out
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>>>>>>> origin/tester_1
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def copy_conv_weights_from(self, source):
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"""Tie convolutional layers"""
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# only tie conv layers
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@ -416,15 +416,14 @@ class SacAeAgent(object):
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h_dist_enc = torch.distributions.Normal(h_mu, h_logvar.exp())
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h_dist_pred = torch.distributions.Normal(mean, std)
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enc_loss = torch.distributions.kl.kl_divergence(h_dist_enc, h_dist_pred).mean() * 1e-2
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"""
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with torch.no_grad():
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z_pos, _ , _ = self.critic_target.encoder(next_obs_list[-1])
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z_out = self.critic_target.encoder.combine(torch.concat((z_pos, action), dim=-1))
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logits = self.lb_loss.compute_logits(h, z_out)
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labels = torch.arange(logits.shape[0]).long().to(self.device)
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lb_loss = nn.CrossEntropyLoss()(logits, labels) * 1e-2
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"""
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#with torch.no_grad():
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# z_pos, _ , _ = self.critic.encoder(next_obs_list[-1])
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#ub_loss = club_loss(state_enc["sample"], mean, state_enc["logvar"], h) * 1e-1
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@ -437,7 +436,7 @@ class SacAeAgent(object):
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ub_loss = torch.tensor(0.0)
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#enc_loss = torch.tensor(0.0)
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lb_loss = torch.tensor(0.0)
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#lb_loss = torch.tensor(0.0)
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#rec_loss = torch.tensor(0.0)
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loss = rec_loss + enc_loss + lb_loss + ub_loss
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self.encoder_optimizer.zero_grad()
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3
train.py
3
train.py
@ -28,10 +28,7 @@ def parse_args():
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parser.add_argument('--frame_stack', default=3, type=int)
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parser.add_argument('--img_source', default=None, type=str, choices=['color', 'noise', 'images', 'video', 'none'])
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parser.add_argument('--resource_files', type=str)
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<<<<<<< HEAD
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parser.add_argument('--resource_files_test', type=str)
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=======
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>>>>>>> origin/tester_1
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parser.add_argument('--total_frames', default=10000, type=int)
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# replay buffer
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parser.add_argument('--replay_buffer_capacity', default=100000, type=int)
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