--- a
+++ b/SAC/model.py
@@ -0,0 +1,205 @@
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from torch.distributions import Normal
+from torch.nn.utils.rnn import pack_padded_sequence, pad_packed_sequence
+
+LOG_SIG_MAX = 2
+LOG_SIG_MIN = -20
+epsilon = 1e-6
+
+# Initialize Policy weights
+def weights_init_(m):
+    if isinstance(m, nn.Linear):
+        torch.nn.init.xavier_uniform_(m.weight, gain=1)
+        torch.nn.init.constant_(m.bias, 0)
+
+class Actor(nn.Module):
+    def __init__(self, num_inputs, num_actions, hidden_dim, model, action_space=None):
+        super(Actor, self).__init__()
+
+        self.linear1 = nn.Linear(num_inputs, hidden_dim)
+
+        if model == "rnn":
+            self.rnn = nn.RNN(hidden_dim, hidden_dim, batch_first=True)
+        elif model == "gru":
+            self.rnn = nn.GRU(hidden_dim, hidden_dim, batch_first=True)
+        else:
+            raise NotImplementedError
+
+        self.mean_linear = nn.Linear(hidden_dim, num_actions)
+        self.log_std_linear = nn.Linear(hidden_dim, num_actions)
+
+        self.apply(weights_init_)
+
+        # action rescaling
+        # Pass none action space and adjust the action scale and bias manually
+        if action_space is None:
+            self.action_scale = torch.tensor(0.5)
+            self.action_bias = torch.tensor(0.5)
+        else:
+            self.action_scale = torch.FloatTensor(
+                (action_space.high - action_space.low) / 2.)
+            self.action_bias = torch.FloatTensor(
+                (action_space.high + action_space.low) / 2.)
+
+    def forward(self, state, h_prev, sampling, len_seq= None):
+
+        x = F.tanh(self.linear1(state))
+
+        if sampling == False:
+            assert len_seq!=None, "Proved the len_seq"
+            x = pack_padded_sequence(x, len_seq, batch_first= True, enforce_sorted= False)
+
+        #Tap RNN input for fixedpoint analysis
+        rnn_in = x
+
+        x, (h_current) = self.rnn(x, (h_prev))
+
+        if sampling == False:
+           x, _ = pad_packed_sequence(x, batch_first= True)
+
+        if sampling == True:
+            x = x.squeeze(1)
+
+        # x = F.relu(x)
+        mean = self.mean_linear(x)
+        log_std = self.log_std_linear(x)
+        log_std = torch.clamp(log_std, min=LOG_SIG_MIN, max=LOG_SIG_MAX)
+
+        return mean, log_std, h_current, x, rnn_in
+
+    def sample(self, state, h_prev, sampling, len_seq=None):
+
+        mean, log_std, h_current, x, rnn_in = self.forward(state, h_prev, sampling, len_seq)
+        #if sampling == False; then mask the mean and log_std using len_seq
+        if sampling == False:
+            assert mean.size()[1] == log_std.size()[1], "There is a mismatch between and mean and sigma Sl_max"
+            sl_max = mean.size()[1]
+            with torch.no_grad():
+                for seq_idx, k in enumerate(len_seq):
+                    for j in range(1, sl_max + 1):
+                        if j <= k:
+                            if seq_idx == 0 and j == 1:
+                                mask_seq = torch.tensor([True], dtype=bool)
+                            else:
+                                mask_seq = torch.cat((mask_seq, torch.tensor([True])), dim=0)
+                        else:
+                            mask_seq = torch.cat((mask_seq, torch.tensor([False])), dim=0)
+            #The mask has been created, Now filter the mean and sigma using this mask
+            mean = mean.reshape(-1, mean.size()[-1])[mask_seq]
+            log_std = log_std.reshape(-1, log_std.size()[-1])[mask_seq]
+
+        if sampling == True:
+            mask_seq = [] #If sampling is True return a dummy mask seq
+
+        std = log_std.exp()
+
+        # white noise
+        normal = Normal(mean, std)
+        noise = normal.rsample()
+
+        # reparameterization trick
+        y_t = torch.tanh(noise) 
+        action = y_t * self.action_scale + self.action_bias
+        log_prob = normal.log_prob(noise)
+
+        # Enforce the action_bounds
+        log_prob -= torch.log(self.action_scale * (1 - y_t.pow(2)) + epsilon)
+        log_prob = log_prob.sum(1, keepdim=True)
+        mean = torch.tanh(mean) * self.action_scale + self.action_bias
+
+        return action, log_prob, mean, h_current, mask_seq, x, rnn_in
+
+    def forward_for_simple_dynamics(self, state, h_prev, sampling, len_seq= None):
+
+        x = F.tanh(self.linear1(state))
+
+        #Tap the output of the first linear layer
+        x_l1 = x
+
+        if sampling == False:
+            assert len_seq!=None, "Proved the len_seq"
+            x = pack_padded_sequence(x, len_seq, batch_first= True, enforce_sorted= False)
+
+        x, _ = self.rnn(x, (h_prev))
+
+        if sampling == False:
+           x, _ = pad_packed_sequence(x, batch_first= True)
+
+        # x = F.relu(x)
+
+        return x, x_l1
+
+
+    def forward_lstm(self, state, h_prev, sampling, len_seq= None):
+
+        x = F.tanh(self.linear1(state))
+
+        if sampling == False:
+            assert len_seq!=None, "Proved the len_seq"
+
+            x = pack_padded_sequence(x, len_seq, batch_first= True, enforce_sorted= False)
+
+        x, (h_current) = self.rnn(x, (h_prev))
+
+        if sampling == False:
+           x, len_x_seq = pad_packed_sequence(x, batch_first= True)
+
+        if sampling == True:
+            x = x.squeeze(1)
+
+        return x
+
+    def forward_for_neural_pert(self, state, h_prev, neural_pert= None):
+
+        x = F.tanh(self.linear1(state))
+
+        #Tap RNN input for fixedpoint analysis
+        rnn_in = x
+
+        x, (h_current) = self.rnn(x, (h_prev))
+
+        #Add the neural perturbation to the RNN output
+        x = x+neural_pert
+
+        x = x.squeeze(1)
+
+        mean = self.mean_linear(x)
+        log_std = self.log_std_linear(x)
+        log_std = torch.clamp(log_std, min=LOG_SIG_MIN, max=LOG_SIG_MAX)
+
+
+        action = torch.tanh(mean) * self.action_scale + self.action_bias
+
+        return action.detach().cpu().numpy()[0], h_current.detach(), x.detach().cpu().numpy(), rnn_in.detach().cpu().numpy()
+
+class Critic(nn.Module):
+    def __init__(self, num_inputs, num_actions, hidden_dim):
+        super(Critic, self).__init__()
+
+        # Q1 architecture
+        self.linear1 = nn.Linear(num_inputs + num_actions, hidden_dim)
+        self.linear2 = nn.Linear(hidden_dim, hidden_dim)
+        self.linear3 = nn.Linear(hidden_dim, 1)
+
+        # Q2 architecture
+        self.linear4 = nn.Linear(num_inputs + num_actions, hidden_dim)
+        self.linear5 = nn.Linear(hidden_dim, hidden_dim)
+        self.linear6 = nn.Linear(hidden_dim, 1)
+
+        self.apply(weights_init_)
+
+    def forward(self, state, action):
+
+        xu = torch.cat([state, action], 1)
+        
+        x1 = F.relu(self.linear1(xu))
+        x1 = F.relu(self.linear2(x1))
+        x1 = self.linear3(x1)
+
+        x2 = F.relu(self.linear4(xu))
+        x2 = F.relu(self.linear5(x2))
+        x2 = self.linear6(x2)
+
+        return x1, x2
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