Merge remote-tracking branch 'origin/master' into MLA-1734-demo-provider

README.md

 | **Release 8** | October 14, 2020 | [source](https://github.com/Unity-Technologies/ml-agents/tree/release_8) | [docs](https://github.com/Unity-Technologies/ml-agents/tree/release_8_docs/docs/Readme.md) | [download](https://github.com/Unity-Technologies/ml-agents/archive/release_8.zip) | [0.21.0](https://pypi.org/project/mlagents/0.21.0/) | [1.5.0](https://docs.unity3d.com/Packages/com.unity.ml-agents@1.5/manual/index.html) |
 | **Verified Package 1.0.5** | September 23, 2020 | [source](https://github.com/Unity-Technologies/ml-agents/tree/com.unity.ml-agents_1.0.5) | [docs](https://github.com/Unity-Technologies/ml-agents/blob/release_2_verified_docs/docs/Readme.md) | [download](https://github.com/Unity-Technologies/ml-agents/archive/com.unity.ml-agents_1.0.5.zip) | [0.16.1](https://pypi.org/project/mlagents/0.16.1/) | [1.0.5](https://docs.unity3d.com/Packages/com.unity.ml-agents@1.0/manual/index.html) |
 | **Release 7** | September 16, 2020 | [source](https://github.com/Unity-Technologies/ml-agents/tree/release_7) | [docs](https://github.com/Unity-Technologies/ml-agents/tree/release_7_docs/docs/Readme.md) | [download](https://github.com/Unity-Technologies/ml-agents/archive/release_7.zip) | [0.20.0](https://pypi.org/project/mlagents/0.20.0/) | [1.4.0](https://docs.unity3d.com/Packages/com.unity.ml-agents@1.4/manual/index.html) |
+
 If you are a researcher interested in a discussion of Unity as an AI platform,
 see a pre-print of our
 [reference paper on Unity and the ML-Agents Toolkit](https://arxiv.org/abs/1809.02627).

ml-agents-envs/mlagents_envs/base_env.py

     spaces for a group of Agents under the same behavior.
     - observation_specs is a List of ObservationSpec NamedTuple containing
     information about the information of the Agent's observations such as their shapes.
-    The order of the SensorSpec is the same as the order of the observations of an
+    The order of the ObservationSpec is the same as the order of the observations of an
     agent.
     - action_spec is an ActionSpec NamedTuple.
     """

ml-agents-envs/mlagents_envs/rpc_utils.py

         observation_specs.append(
             ObservationSpec(
                 tuple(obs.shape),
-                tuple(DimensionProperty(dim) for dim in obs.dimension_properties),
+                tuple(DimensionProperty(dim) for dim in obs.dimension_properties)
+                if len(obs.dimension_properties) > 0
+                else (DimensionProperty.UNSPECIFIED,) * len(obs.shape),
+
     # proto from communicator < v1.3 does not set action spec, use deprecated fields instead
     if (
         brain_param_proto.action_spec.num_continuous_actions == 0

ml-agents/mlagents/trainers/tests/dummy_config.py

     obs_specs: List[ObservationSpec] = []
     for shape in shapes:
         dim_prop = (DimensionProperty.UNSPECIFIED,) * len(shape)
+        if len(shape) == 2:
+            dim_prop = (DimensionProperty.VARIABLE_SIZE, DimensionProperty.NONE)
         spec = ObservationSpec(shape, dim_prop, ObservationType.DEFAULT)
         obs_specs.append(spec)
     return obs_specs

ml-agents/mlagents/trainers/tests/simple_test_envs.py

 
 OBS_SIZE = 1
 VIS_OBS_SIZE = (20, 20, 3)
+VAR_LEN_SIZE = (10, 5)
 STEP_SIZE = 0.2
 
 TIME_PENALTY = 0.01
         step_size=STEP_SIZE,
         num_visual=0,
         num_vector=1,
+        num_var_len=0,
+        var_len_obs_size=VAR_LEN_SIZE,
+        self.num_var_len = num_var_len
+        self.var_len_obs_size = var_len_obs_size
         continuous_action_size, discrete_action_size = action_sizes
         discrete_tuple = tuple(2 for _ in range(discrete_action_size))
         action_spec = ActionSpec(continuous_action_size, discrete_tuple)
             obs_shape.append((self.vec_obs_size,))
         for _ in range(self.num_visual):
             obs_shape.append(self.vis_obs_size)
+        for _ in range(self.num_var_len):
+            obs_shape.append(self.var_len_obs_size)
         obs_spec = create_observation_specs_with_shapes(obs_shape)
         return obs_spec
 
             obs.append(np.ones((1, self.vec_obs_size), dtype=np.float32) * value)
         for _ in range(self.num_visual):
             obs.append(np.ones((1,) + self.vis_obs_size, dtype=np.float32) * value)
+        for _ in range(self.num_var_len):
+            obs.append(np.ones((1,) + self.var_len_obs_size, dtype=np.float32) * value)
         return obs
 
     @property

ml-agents/mlagents/trainers/tests/torch/test_attention.py

+import pytest
 from mlagents.torch_utils import torch
 import numpy as np
 
     MultiHeadAttention,
-    EntityEmbeddings,
+    EntityEmbedding,
+    get_zero_entities_mask,
 )
 
 
     input_1 = generate_input_helper(masking_pattern_1)
     input_2 = generate_input_helper(masking_pattern_2)
 
-    masks = EntityEmbeddings.get_masks([input_1, input_2])
+    masks = get_zero_entities_mask([input_1, input_2])
     assert len(masks) == 2
     masks_1 = masks[0]
     masks_2 = masks[1]
         assert masks_2[0, 1] == 0 if i % 2 == 0 else 1
 
 
+@pytest.mark.parametrize("mask_value", [0, 1])
+def test_all_masking(mask_value):
+    # We make sure that a mask of all zeros or all ones will not trigger an error
+    np.random.seed(1336)
+    torch.manual_seed(1336)
+    size, n_k, = 3, 5
+    embedding_size = 64
+    entity_embeddings = EntityEmbedding(size, n_k, embedding_size)
+    entity_embeddings.add_self_embedding(size)
+    transformer = ResidualSelfAttention(embedding_size, n_k)
+    l_layer = linear_layer(embedding_size, size)
+    optimizer = torch.optim.Adam(
+        list(entity_embeddings.parameters())
+        + list(transformer.parameters())
+        + list(l_layer.parameters()),
+        lr=0.001,
+        weight_decay=1e-6,
+    )
+    batch_size = 20
+    for _ in range(5):
+        center = torch.rand((batch_size, size))
+        key = torch.rand((batch_size, n_k, size))
+        with torch.no_grad():
+            # create the target : The key closest to the query in euclidean distance
+            distance = torch.sum(
+                (center.reshape((batch_size, 1, size)) - key) ** 2, dim=2
+            )
+            argmin = torch.argmin(distance, dim=1)
+            target = []
+            for i in range(batch_size):
+                target += [key[i, argmin[i], :]]
+            target = torch.stack(target, dim=0)
+            target = target.detach()
+
+        embeddings = entity_embeddings(center, key)
+        masks = [torch.ones_like(key[:, :, 0]) * mask_value]
+        prediction = transformer.forward(embeddings, masks)
+        prediction = l_layer(prediction)
+        prediction = prediction.reshape((batch_size, size))
+        error = torch.mean((prediction - target) ** 2, dim=1)
+        error = torch.mean(error) / 2
+        optimizer.zero_grad()
+        error.backward()
+        optimizer.step()
+
+
-    entity_embeddings = EntityEmbeddings(size, [size], embedding_size, [n_k])
-    transformer = ResidualSelfAttention(embedding_size, [n_k])
+    entity_embeddings = EntityEmbedding(size, n_k, embedding_size)
+    entity_embeddings.add_self_embedding(size)
+    transformer = ResidualSelfAttention(embedding_size, n_k)
     l_layer = linear_layer(embedding_size, size)
     optimizer = torch.optim.Adam(
         list(entity_embeddings.parameters())
             target = torch.stack(target, dim=0)
             target = target.detach()
 
-        embeddings = entity_embeddings(center, [key])
-        masks = EntityEmbeddings.get_masks([key])
+        embeddings = entity_embeddings(center, key)
+        masks = get_zero_entities_mask([key])
         prediction = transformer.forward(embeddings, masks)
         prediction = l_layer(prediction)
         prediction = prediction.reshape((batch_size, size))
     n_k = 5
     size = n_k + 1
     embedding_size = 64
-    entity_embeddings = EntityEmbeddings(
-        size, [size], embedding_size, [n_k], concat_self=False
-    )
+    entity_embedding = EntityEmbedding(size, n_k, embedding_size)  # no self
-        list(entity_embeddings.parameters())
+        list(entity_embedding.parameters())
         + list(transformer.parameters())
         + list(l_layer.parameters()),
         lr=0.001,
         sliced_oh = onehots[:, : num + 1]
         inp = torch.cat([inp, sliced_oh], dim=2)
 
-        embeddings = entity_embeddings(inp, [inp])
-        masks = EntityEmbeddings.get_masks([inp])
+        embeddings = entity_embedding(inp, inp)
+        masks = get_zero_entities_mask([inp])
         prediction = transformer(embeddings, masks)
         prediction = l_layer(prediction)
         ce = loss(prediction, argmin)

ml-agents/mlagents/trainers/tests/torch/test_simple_rl.py

     check_environment_trains(env, {BRAIN_NAME: config})
 
 
+@pytest.mark.parametrize("action_sizes", [(0, 1), (1, 0)])
+@pytest.mark.parametrize("num_var_len", [1, 2])
+@pytest.mark.parametrize("num_vector", [0, 1])
+@pytest.mark.parametrize("num_vis", [0, 1])
+def test_var_len_obs_ppo(num_vis, num_vector, num_var_len, action_sizes):
+    env = SimpleEnvironment(
+        [BRAIN_NAME],
+        action_sizes=action_sizes,
+        num_visual=num_vis,
+        num_vector=num_vector,
+        num_var_len=num_var_len,
+        step_size=0.2,
+    )
+    new_hyperparams = attr.evolve(
+        PPO_TORCH_CONFIG.hyperparameters, learning_rate=3.0e-4
+    )
+    config = attr.evolve(PPO_TORCH_CONFIG, hyperparameters=new_hyperparams)
+    check_environment_trains(env, {BRAIN_NAME: config})
+
+
 @pytest.mark.parametrize("num_visual", [1, 2])
 @pytest.mark.parametrize("vis_encode_type", ["resnet", "nature_cnn", "match3"])
 def test_visual_advanced_ppo(vis_encode_type, num_visual):
         [BRAIN_NAME],
         action_sizes=action_sizes,
         num_visual=num_visual,
+        num_vector=0,
+        step_size=0.2,
+    )
+    new_hyperparams = attr.evolve(
+        SAC_TORCH_CONFIG.hyperparameters, batch_size=16, learning_rate=3e-4
+    )
+    config = attr.evolve(SAC_TORCH_CONFIG, hyperparameters=new_hyperparams)
+    check_environment_trains(env, {BRAIN_NAME: config})
+
+
+@pytest.mark.parametrize("action_sizes", [(0, 1), (1, 0)])
+@pytest.mark.parametrize("num_var_len", [1, 2])
+def test_var_len_obs_sac(num_var_len, action_sizes):
+    env = SimpleEnvironment(
+        [BRAIN_NAME],
+        action_sizes=action_sizes,
+        num_visual=0,
+        num_var_len=num_var_len,
         num_vector=0,
         step_size=0.2,
     )

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

 from mlagents.trainers.exception import UnityTrainerException
 
 
-class MultiHeadAttention(torch.nn.Module):
+def get_zero_entities_mask(observations: List[torch.Tensor]) -> List[torch.Tensor]:
-    Multi Head Attention module. We do not use the regular Torch implementation since
-    Barracuda does not support some operators it uses.
-    Takes as input to the forward method 3 tensors:
-     - query: of dimensions (batch_size, number_of_queries, embedding_size)
-     - key: of dimensions (batch_size, number_of_keys, embedding_size)
-     - value: of dimensions (batch_size, number_of_keys, embedding_size)
-    The forward method will return 2 tensors:
-     - The output: (batch_size, number_of_queries, embedding_size)
-     - The attention matrix: (batch_size, num_heads, number_of_queries, number_of_keys)
+    Takes a List of Tensors and returns a List of mask Tensor with 1 if the input was
+    all zeros (on dimension 2) and 0 otherwise. This is used in the Attention
+    layer to mask the padding observations.
+    with torch.no_grad():
+        # Generate the masking tensors for each entities tensor (mask only if all zeros)
+        key_masks: List[torch.Tensor] = [
+            (torch.sum(ent ** 2, axis=2) < 0.01).float() for ent in observations
+        ]
+    return key_masks
+
+
+class MultiHeadAttention(torch.nn.Module):
+        """
+        Multi Head Attention module. We do not use the regular Torch implementation since
+        Barracuda does not support some operators it uses.
+        Takes as input to the forward method 3 tensors:
+        - query: of dimensions (batch_size, number_of_queries, embedding_size)
+        - key: of dimensions (batch_size, number_of_keys, embedding_size)
+        - value: of dimensions (batch_size, number_of_keys, embedding_size)
+        The forward method will return 2 tensors:
+        - The output: (batch_size, number_of_queries, embedding_size)
+        - The attention matrix: (batch_size, num_heads, number_of_queries, number_of_keys)
+        :param embedding_size: The size of the embeddings that will be generated (should be
+        dividable by the num_heads)
+        :param total_max_elements: The maximum total number of entities that can be passed to
+        the module
+        :param num_heads: The number of heads of the attention module
+        """
         super().__init__()
         self.n_heads = num_heads
         self.head_size: int = embedding_size // self.n_heads
         return value_attention, att
 
 
-class EntityEmbeddings(torch.nn.Module):
+class EntityEmbedding(torch.nn.Module):
     """
     A module used to embed entities before passing them to a self-attention block.
     Used in conjunction with ResidualSelfAttention to encode information about a self
 
     def __init__(
         self,
-        x_self_size: int,
-        entity_sizes: List[int],
+        entity_size: int,
+        entity_num_max_elements: Optional[int],
-        entity_num_max_elements: Optional[List[int]] = None,
-        concat_self: bool = True,
-        Constructs an EntityEmbeddings module.
+        Constructs an EntityEmbedding module.
-        :param entity_sizes: List of sizes for other entities. Should be of length
-            equivalent to the number of entities.
-        :param embedding_size: Embedding size for entity encoders.
-        :param entity_num_max_elements: Maximum elements in an entity, None for unrestricted.
+        :param entity_size: Size of other entities.
+        :param entity_num_max_elements: Maximum elements for a given entity, None for unrestricted.
-        :param concat_self: Whether to concatenate x_self to entites. Set True for ego-centric
+        :param embedding_size: Embedding size for the entity encoder.
+        :param concat_self: Whether to concatenate x_self to entities. Set True for ego-centric
-        self.self_size: int = x_self_size
-        self.entity_sizes: List[int] = entity_sizes
-        self.entity_num_max_elements: List[int] = [-1] * len(entity_sizes)
+        self.self_size: int = 0
+        self.entity_size: int = entity_size
+        self.entity_num_max_elements: int = -1
+        self.embedding_size = embedding_size
+        # Initialization scheme from http://www.cs.toronto.edu/~mvolkovs/ICML2020_tfixup.pdf
+        self.self_ent_encoder = LinearEncoder(
+            self.entity_size,
+            1,
+            self.embedding_size,
+            kernel_init=Initialization.Normal,
+            kernel_gain=(0.125 / self.embedding_size) ** 0.5,
+        )
-        self.concat_self: bool = concat_self
-        # If not concatenating self, input to encoder is just entity size
-        if not concat_self:
-            self.self_size = 0
-        # Initialization scheme from http://www.cs.toronto.edu/~mvolkovs/ICML2020_tfixup.pdf
-        self.ent_encoders = torch.nn.ModuleList(
-            [
-                LinearEncoder(
-                    self.self_size + ent_size,
-                    1,
-                    embedding_size,
-                    kernel_init=Initialization.Normal,
-                    kernel_gain=(0.125 / embedding_size) ** 0.5,
-                )
-                for ent_size in self.entity_sizes
-            ]
+    def add_self_embedding(self, size: int) -> None:
+        self.self_size = size
+        self.self_ent_encoder = LinearEncoder(
+            self.self_size + self.entity_size,
+            1,
+            self.embedding_size,
+            kernel_init=Initialization.Normal,
+            kernel_gain=(0.125 / self.embedding_size) ** 0.5,
-        self.embedding_norm = LayerNorm()
-    def forward(
-        self, x_self: torch.Tensor, entities: List[torch.Tensor]
-    ) -> Tuple[torch.Tensor, int]:
-        if self.concat_self:
+    def forward(self, x_self: torch.Tensor, entities: torch.Tensor) -> torch.Tensor:
+        if self.self_size > 0:
+            num_entities = self.entity_num_max_elements
+            if num_entities < 0:
+                if exporting_to_onnx.is_exporting():
+                    raise UnityTrainerException(
+                        "Trying to export an attention mechanism that doesn't have a set max \
+                        number of elements."
+                    )
+                num_entities = entities.shape[1]
+            expanded_self = x_self.reshape(-1, 1, self.self_size)
+            expanded_self = torch.cat([expanded_self] * num_entities, dim=1)
-            self_and_ent: List[torch.Tensor] = []
-            for num_entities, ent in zip(self.entity_num_max_elements, entities):
-                if num_entities < 0:
-                    if exporting_to_onnx.is_exporting():
-                        raise UnityTrainerException(
-                            "Trying to export an attention mechanism that doesn't have a set max \
-                            number of elements."
-                        )
-                    num_entities = ent.shape[1]
-                expanded_self = x_self.reshape(-1, 1, self.self_size)
-                expanded_self = torch.cat([expanded_self] * num_entities, dim=1)
-                self_and_ent.append(torch.cat([expanded_self, ent], dim=2))
-        else:
-            self_and_ent = entities
-            # Encode and concatenate entites
-        encoded_entities = torch.cat(
-            [ent_encoder(x) for ent_encoder, x in zip(self.ent_encoders, self_and_ent)],
-            dim=1,
-        )
-        encoded_entities = self.embedding_norm(encoded_entities)
+            entities = torch.cat([expanded_self, entities], dim=2)
+        # Encode entities
+        encoded_entities = self.self_ent_encoder(entities)
-    @staticmethod
-    def get_masks(observations: List[torch.Tensor]) -> List[torch.Tensor]:
-        """
-        Takes a List of Tensors and returns a List of mask Tensor with 1 if the input was
-        all zeros (on dimension 2) and 0 otherwise. This is used in the Attention
-        layer to mask the padding observations.
-        """
-        with torch.no_grad():
-            # Generate the masking tensors for each entities tensor (mask only if all zeros)
-            key_masks: List[torch.Tensor] = [
-                (torch.sum(ent ** 2, axis=2) < 0.01).float() for ent in observations
-            ]
-        return key_masks
-
-    with an EntityEmbeddings module, to apply multi head self attention to encode information
+    with an EntityEmbedding module, to apply multi head self attention to encode information
     about a "Self" and a list of relevant "Entities".
     """
 
         self,
         embedding_size: int,
-        entity_num_max_elements: Optional[List[int]] = None,
+        entity_num_max_elements: Optional[int] = None,
         num_heads: int = 4,
     ):
         """
         super().__init__()
         self.max_num_ent: Optional[int] = None
         if entity_num_max_elements is not None:
-            _entity_num_max_elements = entity_num_max_elements
-            self.max_num_ent = sum(_entity_num_max_elements)
+            self.max_num_ent = entity_num_max_elements
 
         self.attention = MultiHeadAttention(
             num_heads=num_heads, embedding_size=embedding_size
             kernel_init=Initialization.Normal,
             kernel_gain=(0.125 / embedding_size) ** 0.5,
         )
+        self.embedding_norm = LayerNorm()
+
+        inp = self.embedding_norm(inp)
         # Feed to self attention
         query = self.fc_q(inp)  # (b, n_q, emb)
         key = self.fc_k(inp)  # (b, n_k, emb)

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

         # Any multi-dimentional input should follow that otherwise will
         # cause problem to barracuda import.
         self.policy = policy
+        observation_specs = self.policy.behavior_spec.observation_specs
-        for sens_spec in self.policy.behavior_spec.observation_specs:
-            if len(sens_spec.shape) == 1:
-                vec_obs_size += sens_spec.shape[0]
+        for obs_spec in observation_specs:
+            if len(obs_spec.shape) == 1:
+                vec_obs_size += obs_spec.shape[0]
-            1
-            for sens_spec in self.policy.behavior_spec.observation_specs
-            if len(sens_spec.shape) == 3
+            1 for obs_spec in observation_specs if len(obs_spec.shape) == 3
-        # (It's NHWC in self.policy.behavior_spec.observation_specs.shape)
+        # (It's NHWC in observation_specs.shape)
-            for obs_spec in self.policy.behavior_spec.observation_specs
+            for obs_spec in observation_specs
+
+        dummy_var_len_obs = [
+            torch.zeros(batch_dim + [obs_spec.shape[0], obs_spec.shape[1]])
+            for obs_spec in observation_specs
+            if len(obs_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,
+        )
+
+        self.input_names = ["vector_observation"]
+        for i in range(num_vis_obs):
+            self.input_names.append(f"visual_observation_{i}")
+        for i, obs_spec in enumerate(observation_specs):
+            if len(obs_spec.shape) == 2:
+                self.input_names.append(f"obs_{i}")
+        self.input_names += ["action_masks", "memories"]
-        self.input_names = (
-            ["vector_observation"]
-            + [f"visual_observation_{i}" for i in range(num_vis_obs)]
-            + ["action_masks", "memories"]
-        )
         self.dynamic_axes = {name: {0: "batch"} for name in self.input_names}
 
         self.output_names = ["version_number", "memory_size"]

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

 from mlagents.trainers.settings import NetworkSettings
 from mlagents.trainers.torch.utils import ModelUtils
 from mlagents.trainers.torch.decoders import ValueHeads
-from mlagents.trainers.torch.layers import LSTM, LinearEncoder
+from mlagents.trainers.torch.layers import LSTM, LinearEncoder, Initialization
+from mlagents.trainers.torch.attention import (
+    EntityEmbedding,
+    ResidualSelfAttention,
+    get_zero_entities_mask,
+)
 
 
 ActivationFunction = Callable[[torch.Tensor], torch.Tensor]
             normalize=self.normalize,
         )
 
-        total_enc_size = sum(self.embedding_sizes) + encoded_act_size
+        entity_num_max: int = 0
+        var_processors = [p for p in self.processors if isinstance(p, EntityEmbedding)]
+        for processor in var_processors:
+            entity_max: int = processor.entity_num_max_elements
+            # Only adds entity max if it was known at construction
+            if entity_max > 0:
+                entity_num_max += entity_max
+        if len(var_processors) > 0:
+            if sum(self.embedding_sizes):
+                self.x_self_encoder = LinearEncoder(
+                    sum(self.embedding_sizes),
+                    1,
+                    self.h_size,
+                    kernel_init=Initialization.Normal,
+                    kernel_gain=(0.125 / self.h_size) ** 0.5,
+                )
+            self.rsa = ResidualSelfAttention(self.h_size, entity_num_max)
+            total_enc_size = sum(self.embedding_sizes) + self.h_size
+        else:
+            total_enc_size = sum(self.embedding_sizes)
+
+        total_enc_size += encoded_act_size
         self.linear_encoder = LinearEncoder(
             total_enc_size, network_settings.num_layers, self.h_size
         )
         sequence_length: int = 1,
     ) -> Tuple[torch.Tensor, torch.Tensor]:
         encodes = []
+        var_len_processor_inputs: List[Tuple[nn.Module, torch.Tensor]] = []
+
-            obs_input = inputs[idx]
-            processed_obs = processor(obs_input)
-            encodes.append(processed_obs)
+            if not isinstance(processor, EntityEmbedding):
+                # The input can be encoded without having to process other inputs
+                obs_input = inputs[idx]
+                processed_obs = processor(obs_input)
+                encodes.append(processed_obs)
+            else:
+                var_len_processor_inputs.append((processor, inputs[idx]))
+        if len(encodes) != 0:
+            encoded_self = torch.cat(encodes, dim=1)
+            input_exist = True
+        else:
+            input_exist = False
+        if len(var_len_processor_inputs) > 0:
+            # Some inputs need to be processed with a variable length encoder
+            masks = get_zero_entities_mask([p_i[1] for p_i in var_len_processor_inputs])
+            embeddings: List[torch.Tensor] = []
+            processed_self = self.x_self_encoder(encoded_self) if input_exist else None
+            for processor, var_len_input in var_len_processor_inputs:
+                embeddings.append(processor(processed_self, var_len_input))
+            qkv = torch.cat(embeddings, dim=1)
+            attention_embedding = self.rsa(qkv, masks)
+            if not input_exist:
+                encoded_self = torch.cat([attention_embedding], dim=1)
+                input_exist = True
+            else:
+                encoded_self = torch.cat([encoded_self, attention_embedding], dim=1)
-        if len(encodes) == 0:
-            raise Exception("No valid inputs to network.")
+        if not input_exist:
+            raise Exception(
+                "The trainer was unable to process any of the provided inputs. "
+                "Make sure the trained agents has at least one sensor attached to them."
+            )
-        # Constants don't work in Barracuda
-            inputs = torch.cat(encodes + [actions], dim=-1)
-        else:
-            inputs = torch.cat(encodes, dim=-1)
-        encoding = self.linear_encoder(inputs)
+            encoded_self = torch.cat([encoded_self, actions], dim=1)
+        encoding = self.linear_encoder(encoded_self)
 
         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], ...]:
         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 isinstance(enc, EntityEmbedding):
+                inputs.append(var_len_inputs[var_len_index])
+                var_len_index += 1
+            else:  # visual input
+
         # 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

     VectorInput,
 )
 from mlagents.trainers.settings import EncoderType, ScheduleType
+from mlagents.trainers.torch.attention import EntityEmbedding
-from mlagents_envs.base_env import ObservationSpec
+from mlagents_envs.base_env import ObservationSpec, DimensionProperty
 
 
 class ModelUtils:
         EncoderType.NATURE_CNN: 36,
         EncoderType.RESNET: 15,
     }
+
+    VALID_VISUAL_PROP = frozenset(
+        [
+            (
+                DimensionProperty.TRANSLATIONAL_EQUIVARIANCE,
+                DimensionProperty.TRANSLATIONAL_EQUIVARIANCE,
+                DimensionProperty.NONE,
+            ),
+            (DimensionProperty.UNSPECIFIED,) * 3,
+        ]
+    )
+
+    VALID_VECTOR_PROP = frozenset(
+        [(DimensionProperty.NONE,), (DimensionProperty.UNSPECIFIED,)]
+    )
+
+    VALID_VAR_LEN_PROP = frozenset(
+        [(DimensionProperty.VARIABLE_SIZE, DimensionProperty.NONE)]
+    )
 
     @staticmethod
     def update_learning_rate(optim: torch.optim.Optimizer, lr: float) -> None:
 
     @staticmethod
     def get_encoder_for_obs(
-        shape: Tuple[int, ...],
+        obs_spec: ObservationSpec,
         normalize: bool,
         h_size: int,
         vis_encode_type: EncoderType,
         :param h_size: Number of hidden units per layer.
         :param vis_encode_type: Type of visual encoder to use.
         """
-        if len(shape) == 1:
-            # Case rank 1 tensor
-            return (VectorInput(shape[0], normalize), shape[0])
-        if len(shape) == 3:
-            ModelUtils._check_resolution_for_encoder(
-                shape[0], shape[1], vis_encode_type
-            )
+        shape = obs_spec.shape
+        dim_prop = obs_spec.dimension_property
+
+        # VISUAL
+        if dim_prop in ModelUtils.VALID_VISUAL_PROP:
-        raise UnityTrainerException(f"Unsupported shape of {shape} for observation")
+        # VECTOR
+        if dim_prop in ModelUtils.VALID_VECTOR_PROP:
+            return (VectorInput(shape[0], normalize), shape[0])
+        # VARIABLE LENGTH
+        if dim_prop in ModelUtils.VALID_VAR_LEN_PROP:
+            return (
+                EntityEmbedding(
+                    entity_size=shape[1],
+                    entity_num_max_elements=shape[0],
+                    embedding_size=h_size,
+                ),
+                0,
+            )
+        # OTHER
+        raise UnityTrainerException(f"Unsupported Sensor with specs {obs_spec}")
 
     @staticmethod
     def create_input_processors(
         :param unnormalized_inputs: Vector inputs that should not be normalized, and added to the vector
             obs.
         :param normalize: Normalize all vector inputs.
-        :return: Tuple of visual encoders and vector encoders each as a list.
+        :return: Tuple of :
+         - ModuleList of the encoders
+         - A list of embedding sizes (0 if the input requires to be processed with a variable length
+         observation encoder)
-                obs_spec.shape, normalize, h_size, vis_encode_type
+                obs_spec, normalize, h_size, vis_encode_type
+        x_self_size = sum(embedding_sizes)  # The size of the "self" embedding
+        if x_self_size > 0:
+            for enc in encoders:
+                if isinstance(enc, EntityEmbedding):
+                    enc.add_self_embedding(h_size)
         return (nn.ModuleList(encoders), embedding_sizes)
 
     @staticmethod