[Feature] Add Stack transform

.github/unittest/linux_libs/scripts_unity_mlagents/run_test.sh

@@ -23,6 +23,7 @@ conda deactivate && conda activate ./env
 python -c "import mlagents_envs"
 
 python .github/unittest/helpers/coverage_run_parallel.py -m pytest test/test_libs.py --instafail -v --durations 200 --capture no -k TestUnityMLAgents --runslow
+python .github/unittest/helpers/coverage_run_parallel.py -m pytest test/test_transforms.py --instafail -v --durations 200 --capture no -k test_transform_env[unity]
 
 coverage combine
 coverage xml -i

test/mocking_classes.py

@@ -2,7 +2,7 @@
 #
 # This source code is licensed under the MIT license found in the
 # LICENSE file in the root directory of this source tree.
-from typing import Optional
+from typing import Dict, List, Optional
 
 import torch
 import torch.nn as nn
@@ -24,7 +24,12 @@
 from torchrl.data.utils import consolidate_spec
 from torchrl.envs.common import EnvBase
 from torchrl.envs.model_based.common import ModelBasedEnvBase
-from torchrl.envs.utils import _terminated_or_truncated
+from torchrl.envs.utils import (
+    _terminated_or_truncated,
+    check_marl_grouping,
+    MarlGroupMapType,
+)
+
 
 spec_dict = {
     "bounded": Bounded,
@@ -1059,6 +1064,154 @@ def _step(
         return tensordict
 
 
+class MultiAgentCountingEnv(EnvBase):
+    """A multi-agent env that is done after a given number of steps.
+
+    All agents have identical specs.
+
+    The count is incremented by 1 on each step.
+
+    """
+
+    def __init__(
+        self,
+        n_agents: int,
+        group_map: MarlGroupMapType
+        | Dict[str, List[str]] = MarlGroupMapType.ALL_IN_ONE_GROUP,
+        max_steps: int = 5,
+        start_val: int = 0,
+        **kwargs,
+    ):
+        super().__init__(**kwargs)
+        self.max_steps = max_steps
+        self.start_val = start_val
+        self.n_agents = n_agents
+        self.agent_names = [f"agent_{idx}" for idx in range(n_agents)]
+
+        if isinstance(group_map, MarlGroupMapType):
+            group_map = group_map.get_group_map(self.agent_names)
+        check_marl_grouping(group_map, self.agent_names)
+
+        self.group_map = group_map
+
+        observation_specs = {}
+        reward_specs = {}
+        done_specs = {}
+        action_specs = {}
+
+        for group_name, agents in group_map.items():
+            observation_specs[group_name] = {}
+            reward_specs[group_name] = {}
+            done_specs[group_name] = {}
+            action_specs[group_name] = {}
+
+            for agent_name in agents:
+                observation_specs[group_name][agent_name] = Composite(
+                    observation=Unbounded(
+                        (
+                            *self.batch_size,
+                            3,
+                            4,
+                        ),
+                        dtype=torch.float32,
+                        device=self.device,
+                    ),
+                    shape=self.batch_size,
+                    device=self.device,
+                )
+                reward_specs[group_name][agent_name] = Composite(
+                    reward=Unbounded(
+                        (
+                            *self.batch_size,
+                            1,
+                        ),
+                        device=self.device,
+                    ),
+                    shape=self.batch_size,
+                    device=self.device,
+                )
+                done_specs[group_name][agent_name] = Composite(
+                    done=Categorical(
+                        2,
+                        dtype=torch.bool,
+                        shape=(
+                            *self.batch_size,
+                            1,
+                        ),
+                        device=self.device,
+                    ),
+                    shape=self.batch_size,
+                    device=self.device,
+                )
+                action_specs[group_name][agent_name] = Composite(
+                    action=Binary(n=1, shape=[*self.batch_size, 1], device=self.device),
+                    shape=self.batch_size,
+                    device=self.device,
+                )
+
+        self.observation_spec = Composite(observation_specs)
+        self.reward_spec = Composite(reward_specs)
+        self.done_spec = Composite(done_specs)
+        self.action_spec = Composite(action_specs)
+        self.register_buffer(
+            "count",
+            torch.zeros((*self.batch_size, 1), device=self.device, dtype=torch.int),
+        )
+
+    def _set_seed(self, seed: Optional[int]):
+        torch.manual_seed(seed)
+
+    def _reset(self, tensordict: TensorDictBase, **kwargs) -> TensorDictBase:
+        if tensordict is not None and "_reset" in tensordict.keys():
+            _reset = tensordict.get("_reset")
+            self.count[_reset] = self.start_val
+        else:
+            self.count[:] = self.start_val
+
+        source = {}
+        for group_name, agents in self.group_map.items():
+            source[group_name] = {}
+            for agent_name in agents:
+                source[group_name][agent_name] = TensorDict(
+                    source={
+                        "observation": torch.rand(
+                            (*self.batch_size, 3, 4), device=self.device
+                        ),
+                        "done": self.count > self.max_steps,
+                        "terminated": self.count > self.max_steps,
+                    },
+                    batch_size=self.batch_size,
+                    device=self.device,
+                )
+
+        tensordict = TensorDict(source, batch_size=self.batch_size, device=self.device)
+        return tensordict
+
+    def _step(
+        self,
+        tensordict: TensorDictBase,
+    ) -> TensorDictBase:
+        self.count += 1
+        source = {}
+        for group_name, agents in self.group_map.items():
+            source[group_name] = {}
+            for agent_name in agents:
+                source[group_name][agent_name] = TensorDict(
+                    source={
+                        "observation": torch.rand(
+                            (*self.batch_size, 3, 4), device=self.device
+                        ),
+                        "done": self.count > self.max_steps,
+                        "terminated": self.count > self.max_steps,
+                        "reward": torch.zeros_like(self.count, dtype=torch.float),
+                    },
+                    batch_size=self.batch_size,
+                    device=self.device,
+                )
+        tensordict = TensorDict(source, batch_size=self.batch_size, device=self.device)
+        return tensordict
+
+
 class IncrementingEnv(CountingEnv):
     # Same as CountingEnv but always increments the count by 1 regardless of the action.
     def _step(