[Experiment] Bullet hell

com.unity.ml-agents/Runtime/Communicator/RpcCommunicator.cs

                         agentInfoProto.Observations.Add(obsProto);
                     }
                 }
+                foreach (var proto in m_CurrentUnityRlOutput.AgentInfos[behaviorName].Value)
+                {
+                    if (proto.Id == agentInfoProto.Id && !proto.Done && !agentInfoProto.Done )
+                    {
+                        UnityEngine.Debug.LogError("The same AgentId was added twice " + proto.Id + " "+m_CurrentUnityRlOutput.AgentInfos[behaviorName].Value.Count);
+                    }
+                }
                 m_CurrentUnityRlOutput.AgentInfos[behaviorName].Value.Add(agentInfoProto);
             }
 

ml-agents-envs/mlagents_envs/rpc_utils.py

                 behavior_spec.action_spec.discrete_branches
             )
             action_mask = np.split(action_mask, indices, axis=1)
+
+    if len(set(decision_agent_id)) != len(decision_agent_id):
+        ds = DecisionSteps(
+            decision_obs_list, decision_rewards, decision_agent_id, action_mask
+        )
+        print("Error, duplicate agent_ids :", ds)
     return (
         DecisionSteps(
             decision_obs_list, decision_rewards, decision_agent_id, action_mask

ml-agents/mlagents/trainers/agent_processor.py

             self._process_step(
                 ongoing_step, global_id, decision_steps.agent_id_to_index[local_id]
             )
+            if decision_steps.agent_id_to_index[local_id] == 9:
+                print("wrong index", ongoing_step, global_id, decision_steps.agent_id, decision_steps.obs, decision_steps.reward, terminal_steps.obs, terminal_steps.agent_id)
 
         for _gid in action_global_agent_ids:
             # If the ID doesn't have a last step result, the agent just reset,
     ) -> None:
         terminated = isinstance(step, TerminalStep)
         stored_decision_step, idx = self.last_step_result.get(global_id, (None, None))
+        if idx == 9:
+            print("Index was 9", global_id, stored_decision_step.agent_id)
         stored_take_action_outputs = self.last_take_action_outputs.get(global_id, None)
         if not terminated:
             # Index is needed to grab from last_take_action_outputs

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

 from mlagents.torch_utils import torch
 from typing import Tuple, Optional, List
-from mlagents.trainers.torch.layers import LinearEncoder
+from mlagents.trainers.torch.layers import LinearEncoder, linear_layer, Initialization
 
 
 class MultiHeadAttention(torch.nn.Module):
         self.entities_num_max_elements: Optional[List[int]] = None
         self.ent_encoders = torch.nn.ModuleList(
             [
-                LinearEncoder(self.self_size + ent_size, 2, embedding_size)
+                # LinearEncoder(self.self_size + ent_size, 2, embedding_size)
+                # from http://www.cs.toronto.edu/~mvolkovs/ICML2020_tfixup.pdf
+                # linear_layer(self.self_size + ent_size, embedding_size, Initialization.Normal, kernel_gain=1 / (self.self_size + ent_size) ** 0.5)
+                LinearEncoder(self.self_size + ent_size, 1, embedding_size)
                 for ent_size in self.entities_sizes
             ]
         )
         self.residual_layer = LinearEncoder(embedding_size, 1, embedding_size)
         if output_size is None:
             output_size = embedding_size
-        self.x_self_residual_layer = LinearEncoder(
-            embedding_size + x_self_size, 1, output_size
-        )
 
     def forward(
         self,
         denominator = torch.sum(1 - mask, dim=1, keepdim=True) + self.EPISLON
         output = numerator / denominator
         # Residual between x_self and the output of the module
-        output = self.x_self_residual_layer(torch.cat([output, x_self], dim=1))
+        output = torch.cat([output, x_self], dim=1)
         return output
 
     @staticmethod
                 for ent in observations
             ]
         return key_masks
+
+class SmallestAttention(torch.nn.Module):
+    def __init__(
+        self,
+        x_self_size: int,
+        entities_sizes: List[int],
+        embedding_size: int,
+        output_size: Optional[int] = None,
+    ):
+        super().__init__()
+        self.self_size = x_self_size
+        self.entities_sizes = entities_sizes
+        self.entities_num_max_elements: Optional[List[int]] = None
+        self.ent_encoders = torch.nn.ModuleList(
+            [
+                LinearEncoder(self.self_size + ent_size, 2, embedding_size)
+                # LinearEncoder(self.self_size + ent_size, 3, embedding_size)
+                # LinearEncoder(self.self_size + ent_size, 1, embedding_size)
+                for ent_size in self.entities_sizes
+            ]
+        )
+        self.importance_layer = LinearEncoder(embedding_size, 1, 1)
+
+    def forward(
+        self,
+        x_self: torch.Tensor,
+        entities: List[torch.Tensor],
+        key_masks: List[torch.Tensor],
+    ) -> torch.Tensor:
+        # Gather the maximum number of entities information
+        if self.entities_num_max_elements is None:
+            self.entities_num_max_elements = []
+            for ent in entities:
+                self.entities_num_max_elements.append(ent.shape[1])
+        # Concatenate all observations with self
+        self_and_ent: List[torch.Tensor] = []
+        for num_entities, ent in zip(self.entities_num_max_elements, entities):
+            expanded_self = x_self.reshape(-1, 1, self.self_size)
+            # .repeat(
+            #     1, num_entities, 1
+            # )
+            expanded_self = torch.cat([expanded_self] * num_entities, dim=1)
+            self_and_ent.append(torch.cat([expanded_self, ent], dim=2))
+        # Generate the tensor that will serve as query, key and value to self attention
+        qkv = torch.cat(
+            [ent_encoder(x) for ent_encoder, x in zip(self.ent_encoders, self_and_ent)],
+            dim=1,
+        )
+        mask = torch.cat(key_masks, dim=1)
+        # Feed to self attention
+        max_num_ent = sum(self.entities_num_max_elements)
+
+        importance = self.importance_layer(qkv) + mask.unsqueeze(2) * -1e6
+        importance = torch.softmax(importance, dim=1)
+        weighted_qkv = qkv * importance
+
+        output = torch.sum(weighted_qkv, dim=1)
+        output = torch.cat([output, x_self], dim=1)
+
+        return output

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

     XavierGlorotUniform = 2
     KaimingHeNormal = 3  # also known as Variance scaling
     KaimingHeUniform = 4
+    Normal = 5
 
 
 _init_methods = {
     Initialization.KaimingHeNormal: torch.nn.init.kaiming_normal_,
     Initialization.KaimingHeUniform: torch.nn.init.kaiming_uniform_,
+    Initialization.Normal: torch.nn.init.normal_
 }
 
 

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

             for sen_spec in self.policy.behavior_spec.sensor_specs
             if len(sen_spec.shape) == 3
         ]
+
+        dummy_var_len_obs = [
+            torch.zeros(
+                batch_dim + [sen_spec.shape[0], sen_spec.shape[1]]
+            )
+            for sen_spec in self.policy.behavior_spec.sensor_specs
+            if len(sen_spec.shape) == 2
+        ]
+
         dummy_masks = torch.ones(
             batch_dim + [sum(self.policy.behavior_spec.action_spec.discrete_branches)]
         )
 
-        self.dummy_input = (dummy_vec_obs, dummy_vis_obs, dummy_masks, dummy_memories)
+        self.dummy_input = (dummy_vec_obs, dummy_vis_obs, dummy_var_len_obs, dummy_masks, dummy_memories)
+            + [f"obs_{i}" for i, sens_spec in enumerate(self.policy.behavior_spec.sensor_specs) if len(sens_spec.shape) == 2]
             + ["action_masks", "memories"]
         )
         self.dynamic_axes = {name: {0: "batch"} for name in self.input_names}

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

 from mlagents.trainers.torch.encoders import VectorInput
 from mlagents.trainers.buffer import AgentBuffer
 from mlagents.trainers.trajectory import ObsUtil
+from mlagents.trainers.torch.attention import SimpleTransformer, SmallestAttention
 
 
 ActivationFunction = Callable[[torch.Tensor], torch.Tensor]
             else 0
         )
 
-        self.processors, self.embedding_sizes = ModelUtils.create_input_processors(
+        self.processors, self.embedding_sizes, var_len_indices = ModelUtils.create_input_processors(
             sensor_specs,
             self.h_size,
             network_settings.vis_encode_type,
-        total_enc_size = sum(self.embedding_sizes) + encoded_act_size
+        self.use_fc = False
+        if len(var_len_indices) > 0:
+            # there are some variable length observations
+            x_self_len = sum(self.embedding_sizes)
+            entities_sizes = [] # TODO : More robust
+            for idx in var_len_indices:
+                entities_sizes.append(sensor_specs[idx].shape[1])
+
+            # self.x_self_enc = LinearEncoder(6, 2, 64)
+            # self.var_len_obs_enc = LinearEncoder(4, 2, 64)
+            # self.transformer = SimpleTransformer(
+            #     64,
+            #     [64],
+            #     self.h_size,
+            #     self.h_size
+            #     )
+            # self.transformer = SimpleTransformer(
+            #     x_self_len,
+            #     entities_sizes,
+            #     self.h_size,
+            #     self.h_size
+            #     )
+            self.transformer = SmallestAttention(x_self_len, entities_sizes, self.h_size, self.h_size)
+            # self.transformer = SmallestAttention(64, [64], self.h_size, self.h_size)
+            # self.use_fc = True
+
+            total_enc_size = self.h_size + sum(self.embedding_sizes)
+            # total_enc_size = 128#self.h_size + sum(self.embedding_sizes)
+            n_layers = 1
+            if self.use_fc:
+                self.transformer = None
+                total_enc_size = 80  + sum(self.embedding_sizes)
+                n_layers = max(1, network_settings.num_layers + 1)
+        else:
+            self.transformer = None
+            total_enc_size = sum(self.embedding_sizes)
+            n_layers = max(1, network_settings.num_layers)
+
+
+        if total_enc_size == 0:
+            raise Exception("No valid inputs to network.")
+        total_enc_size += encoded_act_size
-            total_enc_size, network_settings.num_layers, self.h_size
+            total_enc_size, n_layers, self.h_size
         )
 
         if self.use_lstm:
         sequence_length: int = 1,
     ) -> Tuple[torch.Tensor, torch.Tensor]:
         encodes = []
+        var_len_inputs = []
-            obs_input = inputs[idx]
-            processed_obs = processor(obs_input)
-            encodes.append(processed_obs)
+            if processor is not None:
+                obs_input = inputs[idx]
+                processed_obs = processor(obs_input)
+                encodes.append(processed_obs)
+            else:
+                var_len_inputs.append(inputs[idx])
+                # var_len_inputs.append(
+                #     self.var_len_obs_enc(inputs[idx])
+                #     )
-        if len(encodes) == 0:
-            raise Exception("No valid inputs to network.")
+        if self.transformer is not None and not self.use_fc:
+            x_self = torch.cat(encodes, dim=1)
+            x_self_encoded = x_self
+            # x_self_encoded = self.x_self_enc(x_self)
-        # Constants don't work in Barracuda
-        if actions is not None:
-            inputs = torch.cat(encodes + [actions], dim=-1)
+            encoded_state = self.transformer(
+                x_self_encoded,
+                var_len_inputs,
+                SimpleTransformer.get_masks(var_len_inputs)
+            )
+            # print("\n\n\nUsing transformer ", self.transformer, "use fc = ", self.use_fc, " x_self.shape=",x_self_encoded.shape," var_len_inputs[0].shape=",var_len_inputs[0].shape," len(var_len_inputs)=",len(var_len_inputs))
-            inputs = torch.cat(encodes, dim=-1)
-        encoding = self.linear_encoder(inputs)
+            encoded_state = torch.cat(encodes, dim=1)
+
+        if self.use_fc:
+            x_self = torch.cat(encodes, dim=1)
+            encoded_state = torch.cat([x_self, inputs[0].reshape(x_self.shape[0], 80)], dim=1)
+
+        if actions is not None:
+            encoded_state = torch.cat([encoded_state, actions], dim=1)
+
+        encoding = self.linear_encoder(encoded_state)
 
         if self.use_lstm:
             # Resize to (batch, sequence length, encoding size)
         self,
         vec_inputs: List[torch.Tensor],
         vis_inputs: List[torch.Tensor],
+        var_len_inputs: List[torch.Tensor],
         masks: Optional[torch.Tensor] = None,
         memories: Optional[torch.Tensor] = None,
     ) -> Tuple[Union[int, torch.Tensor], ...]:
         start = 0
         end = 0
         vis_index = 0
+        var_len_index = 0
         for i, enc in enumerate(self.network_body.processors):
             if isinstance(enc, VectorInput):
                 # This is a vec_obs
                 start = end
-            else:
+            elif enc is not None:
+            else:
+                inputs.append(var_len_inputs[var_len_index])
+                var_len_index+=1
         # End of code to convert the vec and vis obs into a list of inputs for the network
         encoding, memories_out = self.network_body(
             inputs, memories=memories, sequence_length=1

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

         """
         encoders: List[nn.Module] = []
         embedding_sizes: List[int] = []
-        for sen_spec in sensor_specs:
-            encoder, embedding_size = ModelUtils.get_encoder_for_obs(
-                sen_spec.shape, normalize, h_size, vis_encode_type
-            )
-            encoders.append(encoder)
-            embedding_sizes.append(embedding_size)
+        var_len_indices: List[int] = []
+        for idx, sen_spec in enumerate(sensor_specs):
+            if len(sen_spec.shape) == 2:
+                # This is a 2D tensor
+                # TODO : better if condition
+                var_len_indices.append(idx)
+                encoders.append(None)
+                embedding_sizes.append(0)
+            else:
+                encoder, embedding_size = ModelUtils.get_encoder_for_obs(
+                    sen_spec.shape, normalize, h_size, vis_encode_type
+                )
+                encoders.append(encoder)
+                embedding_sizes.append(embedding_size)
-        return (nn.ModuleList(encoders), embedding_sizes)
+        return (nn.ModuleList(encoders), embedding_sizes, var_len_indices)
 
     @staticmethod
     def list_to_tensor(

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config/ppo/Bullet.yaml

+behaviors:
+  Dodge:
+    trainer_type: ppo
+    hyperparameters:
+      batch_size: 1024
+      buffer_size: 10240
+      learning_rate: 0.0003
+      beta: 0.005
+      epsilon: 0.2
+      lambd: 0.95
+      num_epoch: 3
+      learning_rate_schedule: linear
+    network_settings:
+      normalize: true
+      hidden_units: 64
+      num_layers: 2
+      vis_encode_type: simple
+    reward_signals:
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+        gamma: 0.99
+        strength: 1.0
+    keep_checkpoints: 5
+    max_steps: 50000000
+    time_horizon: 64
+    summary_freq: 100000
+    threaded: true

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