diff --git a/rl_games/networks/moe.py b/rl_games/networks/moe.py
new file mode 100644
index 00000000..6c43efb8
--- /dev/null
+++ b/rl_games/networks/moe.py
@@ -0,0 +1,142 @@
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from rl_games.common import networks
+from rl_games.common import layers
+
+class MoENet(networks.NetworkBuilder.BaseNetwork):
+    def __init__(self, params, **kwargs):
+        nn.Module.__init__(self)
+        actions_num = kwargs.pop('actions_num')
+        input_shape = kwargs.pop('input_shape')
+        num_inputs = 0
+
+        self.central_value = params.get('central_value', False)
+        self.value_size = kwargs.pop('value_size', 1)
+
+        # Parameters from params
+        num_experts = params.get('num_experts', 3)
+        hidden_size = params.get('hidden_size', 128)
+        gating_hidden_size = params.get('gating_hidden_size', 64)
+        self.use_sparse_gating = params.get('use_sparse_gating', False)
+        self.top_k = params.get('top_k', 1)
+        self.lambda_entropy = params.get('lambda_entropy', 0.01)
+        self.lambda_diversity = params.get('lambda_diversity', 0.01)
+
+        # Input processing
+        assert isinstance(input_shape, dict), "Input shape must be a dict"
+        for k, v in input_shape.items():
+            num_inputs += v[0]
+
+        # Gating Network
+        self.gating_fc1 = nn.Linear(num_inputs, gating_hidden_size)
+        self.gating_fc2 = nn.Linear(gating_hidden_size, num_experts)
+
+        # Expert Networks
+        self.expert_networks = nn.ModuleList([
+            nn.Sequential(
+                nn.Linear(num_inputs, hidden_size),
+                nn.ReLU(),
+                nn.Linear(hidden_size, hidden_size),
+                nn.ReLU(),
+                nn.Linear(hidden_size, hidden_size),
+                nn.ReLU(),
+            ) for _ in range(num_experts)
+        ])
+
+        # Output layers
+        self.mean_linear = nn.Linear(hidden_size, actions_num)
+        self.value = nn.Linear(hidden_size, self.value_size)
+
+        # Auxiliary loss map
+        self.aux_loss_map = {
+            'entropy_loss': None,
+            'diversity_loss': None,
+        }
+
+    def is_rnn(self):
+        return False
+
+    def get_aux_loss(self):
+        return self.aux_loss_map
+
+    def forward(self, obs_dict):
+        # Combine observations
+        obs = []
+        for k in obs_dict:
+            obs.append(obs_dict[k])
+        obs = torch.cat(obs, dim=-1)
+
+        # Gating Network Forward Pass
+        gating_x = F.relu(self.gating_fc1(obs))
+        gating_logits = self.gating_fc2(gating_x)  # Shape: [batch_size, num_experts]
+        gating_weights = F.softmax(gating_logits, dim=1)
+
+        # Apply Sparse Gating if enabled
+        if self.use_sparse_gating:
+            topk_values, topk_indices = torch.topk(gating_weights, self.top_k, dim=1)
+            sparse_mask = torch.zeros_like(gating_weights)
+            sparse_mask.scatter_(1, topk_indices, topk_values)
+            gating_weights = sparse_mask / sparse_mask.sum(dim=1, keepdim=True)  # Re-normalize
+
+        # Compute Entropy Loss for Gating Weights
+        entropy = -torch.sum(gating_weights * torch.log(gating_weights + 1e-8), dim=1)
+        entropy_loss = torch.mean(entropy)
+        self.aux_loss_map['entropy_loss'] = self.lambda_entropy * entropy_loss
+
+        # Expert Networks Forward Pass
+        expert_outputs = []
+        for expert in self.expert_networks:
+            expert_outputs.append(expert(obs))  # Each output shape: [batch_size, hidden_size]
+        expert_outputs = torch.stack(expert_outputs, dim=1)  # Shape: [batch_size, num_experts, hidden_size]
+
+        # Compute Diversity Loss
+        diversity_loss = 0.0
+        num_experts = len(self.expert_networks)
+        for i in range(num_experts):
+            for j in range(i + 1, num_experts):
+                similarity = F.cosine_similarity(expert_outputs[:, i, :], expert_outputs[:, j, :], dim=-1)
+                diversity_loss += torch.mean(similarity)
+        num_pairs = num_experts * (num_experts - 1) / 2
+        diversity_loss = diversity_loss / num_pairs
+        self.aux_loss_map['diversity_loss'] = self.lambda_diversity * diversity_loss
+
+        # Aggregate Expert Outputs
+        gating_weights = gating_weights.unsqueeze(-1)  # Shape: [batch_size, num_experts, 1]
+        aggregated_output = torch.sum(gating_weights * expert_outputs, dim=1)  # Shape: [batch_size, hidden_size]
+
+        out = aggregated_output
+        value = self.value_act(self.value(out))
+        states = None
+        if self.central_value:
+            return value, states
+
+        if self.is_discrete:
+            logits = self.logits(out)
+            return logits, value, states
+        if self.is_multi_discrete:
+            logits = [logit(out) for logit in self.logits]
+            return logits, value, states
+        if self.is_continuous:
+            mu = self.mu_act(self.mu(out))
+            if self.fixed_sigma:
+                sigma = self.sigma_act(self.sigma)
+            else:
+                sigma = self.sigma_act(self.sigma(out))
+                return mu, mu*0 + sigma, value, states
+
+
+from rl_games.algos_torch.network_builder import NetworkBuilder
+
+class MoENetBuilder(NetworkBuilder):
+    def __init__(self, **kwargs):
+        NetworkBuilder.__init__(self)
+
+    def load(self, params):
+        self.params = params
+
+    def build(self, name, **kwargs):
+        return MoENet(self.params, **kwargs)
+
+    def __call__(self, name, **kwargs):
+        return self.build(name, **kwargs)
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