Fixing some issues

Project/Assets/ML-Agents/Examples/Bullet/Scripts/DodgeAgent.cs

     /// </summary>
     public override void OnEpisodeBegin()
     {
-        var rotation = Random.Range(0, 4);
-        var rotationAngle = rotation * 90f;
-        area.transform.Rotate(new Vector3(0f, rotationAngle, 0f));
+        // var rotation = Random.Range(0, 4);
+        // var rotationAngle = rotation * 90f;
+        // area.transform.Rotate(new Vector3(0f, rotationAngle, 0f));
 
         transform.position = GetRandomSpawnPos();//
         m_AgentRb.velocity = Vector3.zero;

ml-agents/mlagents/trainers/torch/networks.py

             else 0
         )
 
-        self.visual_processors, self.vector_processors, self.attention, encoder_input_size = ModelUtils.create_input_processors(
+        self.visual_processors, self.vector_processors, self.attention, _ = ModelUtils.create_input_processors(
             observation_shapes,
             self.h_size,
             network_settings.vis_encode_type,
-        total_enc_size = encoder_input_size + encoded_act_size
+        # total_enc_size = encoder_input_size + encoded_act_size
-        self.self_embedding = LinearEncoder(6, 1, 64)
-        self.obs_embeding = LinearEncoder(4 + 64, 1, 64)
+        self.self_embedding = LinearEncoder(6, 2, 64)
+        self.obs_embeding = LinearEncoder(4, 2, 64)
-            total_enc_size, network_settings.num_layers, self.h_size
+            64 * 2, network_settings.num_layers, self.h_size
         )
 
         if self.use_lstm:
 
         # TODO : This is a Hack
         var_len_input = vis_inputs[0].reshape(-1, 20, 4)
-        key_mask =  (
+        key_mask = (
-        )  # 1 means mask and 0 means let though
+        ).type(torch.FloatTensor)  # 1 means mask and 0 means let though
-        objects = torch.cat([expanded_x_self, var_len_input], dim=2)  # (b,20,68)
-        obj_encoding = self.obs_embeding(objects)  # (b,20,64)
+        obj_emb = self.obs_embeding(var_len_input)
+        objects = torch.cat([expanded_x_self, obj_emb], dim=2)  # (b,20,64)
+        obj_and_self = self.self_and_obs_embedding(objects)  # (b,20,64)
-        self_and_key_emb = torch.cat(
-            [x_self.reshape(-1, 1, 64), obj_encoding], dim=1
-        )  # (b,21,64)
-        key_mask = torch.cat(
-            [torch.zeros((self_and_key_emb.shape[0], 1)), key_mask], dim=1
-        )  # first one is never masked
+        # self_and_key_emb = torch.cat(
+        #     [x_self.reshape(-1, 1, 64), obj_and_self], dim=1
+        # )  # (b,21,64)
+        # key_mask = torch.cat(
+        #     [torch.zeros((self_and_key_emb.shape[0], 1)), key_mask], dim=1
+        # )  # first one is never masked
+        # output, _ = self.attention(
+        #     self_and_key_emb, self_and_key_emb, self_and_key_emb, key_mask
+        # )  # (b, 21, 64)
-            self_and_key_emb, self_and_key_emb, self_and_key_emb, key_mask
+            obj_and_self, obj_and_self, obj_and_self, key_mask
-        output = self.dense_after_attention(output) + self_and_key_emb
+        output = self.dense_after_attention(output) + obj_and_self
-            output * (1 - key_mask).reshape(-1, 21, 1), dim=1
-        ) / torch.sum(
+            output * (1 - key_mask).reshape(-1, 20, 1), dim=1
+        ) / (torch.sum(
-        )  # average pooling
+        ) + 0.001 )   # average pooling
-        encoding = self.linear_encoder(output + x_self)
+        encoding = self.linear_encoder(torch.cat([output , x_self], dim=1))
 
         if self.use_lstm:
             # Resize to (batch, sequence length, encoding size)