feat: support interface of environment customization

.pre-commit-config.yaml

@@ -29,25 +29,25 @@ repos:
       - id: debug-statements
       - id: double-quote-string-fixer
   - repo: https://github.com/charliermarsh/ruff-pre-commit
-    rev: v0.0.292
+    rev: v0.3.5
     hooks:
       - id: ruff
         args: [--fix, --exit-non-zero-on-fix]
   - repo: https://github.com/PyCQA/isort
-    rev: 5.12.0
+    rev: 5.13.2
     hooks:
       - id: isort
   - repo: https://github.com/psf/black
-    rev: 23.9.1
+    rev: 24.3.0
     hooks:
       - id: black-jupyter
   - repo: https://github.com/asottile/pyupgrade
-    rev: v3.15.0
+    rev: v3.15.2
     hooks:
       - id: pyupgrade
         args: [--py38-plus] # sync with requires-python
   - repo: https://github.com/pycqa/flake8
-    rev: 6.1.0
+    rev: 7.0.0
     hooks:
       - id: flake8
         additional_dependencies:

docs/requirements.txt

@@ -8,3 +8,4 @@ sphinx-autoapi
 sphinx-autobuild
 sphinx-autodoc-typehints
 furo
+sphinxcontrib-spelling

docs/source/envs/custom.rst

@@ -0,0 +1,22 @@
+OmniSafe Customization Interface of Environments
+================================================
+
+.. currentmodule:: omnisafe.envs.custom_env
+
+.. autosummary::
+
+    CustomEnv
+
+CustomEnv
+---------
+
+.. card::
+    :class-header: sd-bg-success sd-text-white
+    :class-card: sd-outline-success  sd-rounded-1
+
+    Documentation
+    ^^^
+
+    .. autoclass:: CustomEnv
+        :members:
+        :private-members:

docs/source/index.rst

@@ -366,6 +366,7 @@ this project, don't hesitate to ask your question on `the GitHub issue page <htt
 
     start/installation
     start/usage
+    start/env
 
 .. toctree::
     :hidden:
@@ -458,6 +459,7 @@ this project, don't hesitate to ask your question on `the GitHub issue page <htt
     :caption: envs api
 
     envs/core
+    envs/custom
     envs/wrapper
     envs/safety_gymnasium
     envs/mujoco_env

docs/source/spelling_wordlist.txt

@@ -485,3 +485,4 @@ UpdateActorCritic
 UpdateDynamics
 mathbb
 meger
+Jupyter

docs/source/start/env.rst

@@ -0,0 +1,73 @@
+Environments Customization
+===========================
+
+OmniSafe supports a flexible environment customization interface. Users only need to make minimal
+interface adaptations within the simplest template provided by OmniSafe to complete the environment
+customization.
+
+.. note::
+    The highlight of OmniSafe's environment customization is that **users only need to modify the code at the environment layer**, to enjoy OmniSafe's complete set of training, saving, and data logging mechanisms. This allows users who install from PyPI to use it easily and only focus on the dynamics of the environment.
+
+
+Get Started with the Simplest Template
+--------------------------------------
+
+OmniSafe offers a minimal implementation of an environment template as an example of a customized
+environments, :doc:`../envs/custom`.
+We recommend reading this template in detail and customizing it based on it.
+
+.. card::
+    :class-header: sd-bg-success sd-text-white
+    :class-card: sd-outline-success  sd-rounded-1
+
+    Frequently Asked Questions
+    ^^^
+    1. What changes are necessary to embed the environment into OmniSafe?
+    2. My environment requires specific parameters; can these be integrated into OmniSafe's parameter mechanism?
+    3. I need to log information during training; how can I achieve this?
+    4. After embedding the environment, how do I run the algorithms in OmniSafe for training?
+
+For the above questions, we provide a complete Jupyter Notebook example (Please see our tutorial on
+GitHub page). We will demonstrate how to start from the most common environments in
+`Gymnasium <https://gymnasium.farama.org/>`_ style, implement
+environment customization and complete the training process.
+
+
+Customization of Your Environments
+-----------------------------------
+
+From Source Code
+^^^^^^^^^^^^^^^^
+
+If you are installing from the source code, you can follow the steps below:
+
+.. card::
+    :class-header: sd-bg-success sd-text-white
+    :class-card: sd-outline-success  sd-rounded-1
+
+    Build from Source Code
+    ^^^
+    1. Create a new file under `omnisafe/envs/`, for example, `omnisafe/envs/my_env.py`.
+    2. Customize the environment in `omnisafe/envs/my_env.py`. Assuming the class name is `MyEnv`, and the environment name is `MyEnv-v0`.
+    3. Add `from .my_env import MyEnv` in `omnisafe/envs/__init__.py`.
+    4. Run the following command in the `omnisafe/examples` folder:
+
+    .. code-block:: bash
+        :linenos:
+
+        python train_policy.py --algo PPOLag --env MyEnv-v0
+
+From PyPI
+^^^^^^^^^
+
+.. card::
+    :class-header: sd-bg-success sd-text-white
+    :class-card: sd-outline-success  sd-rounded-1
+
+    Build from PyPI
+    ^^^
+    1. Customize the environment in any folder. Assuming the class name is `MyEnv`, and the environment name is `MyEnv-v0`.
+    2. Import OmniSafe and the environment registration decorator.
+    3. Run the training.
+
+    For a short but detailed example, please see `examples/train_from_custom_env.py`

examples/train_from_custom_env.py

@@ -0,0 +1,90 @@
+# Copyright 2024 OmniSafe Team. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""Example and template for environment customization."""
+
+from __future__ import annotations
+
+import random
+from typing import Any, ClassVar
+
+import torch
+from gymnasium import spaces
+
+import omnisafe
+from omnisafe.envs.core import CMDP, env_register
+
+
+# first, define the environment class.
+# the most important thing is to add the `env_register` decorator.
+@env_register
+class CustomExampleEnv(CMDP):
+
+    # define what tasks the environment support.
+    _support_envs: ClassVar[list[str]] = ['Custom-v0']
+
+    # automatically reset when `terminated` or `truncated`
+    need_auto_reset_wrapper = True
+    # set `truncated=True` when the total steps exceed the time limit.
+    need_time_limit_wrapper = True
+
+    def __init__(self, env_id: str, **kwargs: dict[str, Any]) -> None:
+        self._count = 0
+        self._num_envs = 1
+        self._observation_space = spaces.Box(low=-1.0, high=1.0, shape=(3,))
+        self._action_space = spaces.Box(low=-1.0, high=1.0, shape=(2,))
+
+    def step(
+        self,
+        action: torch.Tensor,
+    ) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor, dict]:
+        self._count += 1
+        obs = torch.as_tensor(self._observation_space.sample())
+        reward = 2 * torch.as_tensor(random.random())  # noqa
+        cost = 2 * torch.as_tensor(random.random())  # noqa
+        terminated = torch.as_tensor(random.random() > 0.9)  # noqa
+        truncated = torch.as_tensor(self._count > self.max_episode_steps)
+        return obs, reward, cost, terminated, truncated, {'final_observation': obs}
+
+    @property
+    def max_episode_steps(self) -> int:
+        """The max steps per episode."""
+        return 10
+
+    def reset(
+        self,
+        seed: int | None = None,
+        options: dict[str, Any] | None = None,
+    ) -> tuple[torch.Tensor, dict]:
+        self.set_seed(seed)
+        obs = torch.as_tensor(self._observation_space.sample())
+        self._count = 0
+        return obs, {}
+
+    def set_seed(self, seed: int) -> None:
+        random.seed(seed)
+
+    def close(self) -> None:
+        pass
+
+    def render(self) -> Any:
+        pass
+
+
+# Then you can use it like this:
+agent = omnisafe.Agent(
+    'PPOLag',
+    'Custom-v0',
+)
+agent.learn()

omnisafe/adapter/offpolicy_adapter.py

@@ -126,9 +126,7 @@ def rollout(  # pylint: disable=too-many-locals
         """
         for _ in range(rollout_step):
             if use_rand_action:
-                act = torch.as_tensor(self._env.sample_action(), dtype=torch.float32).to(
-                    self._device,
-                )
+                act = (torch.rand(self.action_space.shape) * 2 - 1).unsqueeze(0)  # type: ignore
             else:
                 act = agent.step(self._current_obs, deterministic=False)
             next_obs, reward, cost, terminated, truncated, info = self.step(act)
@@ -181,6 +179,8 @@ def _log_metrics(self, logger: Logger, idx: int) -> None:
             logger (Logger): Logger, to log ``EpRet``, ``EpCost``, ``EpLen``.
             idx (int): The index of the environment.
         """
+        if hasattr(self._env, 'spec_log'):
+            self._env.spec_log(logger)
         logger.store(
             {
                 'Metrics/EpRet': self._ep_ret[idx],

omnisafe/adapter/online_adapter.py

@@ -62,8 +62,14 @@ def __init__(  # pylint: disable=too-many-arguments
         self._cfgs: Config = cfgs
         self._device: torch.device = get_device(cfgs.train_cfgs.device)
         self._env_id: str = env_id
-        self._env: CMDP = make(env_id, num_envs=num_envs, device=self._device)
-        self._eval_env: CMDP = make(env_id, num_envs=1, device=self._device)
+
+        env_cfgs = {}
+
+        if hasattr(self._cfgs, 'env_cfgs') and self._cfgs.env_cfgs is not None:
+            env_cfgs = self._cfgs.env_cfgs.todict()
+
+        self._env: CMDP = make(env_id, num_envs=num_envs, device=self._device, **env_cfgs)
+        self._eval_env: CMDP = make(env_id, num_envs=1, device=self._device, **env_cfgs)
 
         self._wrapper(
             obs_normalize=cfgs.algo_cfgs.obs_normalize,
@@ -109,8 +115,20 @@ def _wrapper(
             cost_normalize (bool, optional): Whether to normalize the cost. Defaults to True.
         """
         if self._env.need_time_limit_wrapper:
-            self._env = TimeLimit(self._env, time_limit=1000, device=self._device)
-            self._eval_env = TimeLimit(self._eval_env, time_limit=1000, device=self._device)
+            assert (
+                self._env.max_episode_steps and self._eval_env.max_episode_steps
+            ), 'You must define max_episode_steps as an integer\
+                or cancel the use of the time_limit wrapper.'
+            self._env = TimeLimit(
+                self._env,
+                time_limit=self._env.max_episode_steps,
+                device=self._device,
+            )
+            self._eval_env = TimeLimit(
+                self._eval_env,
+                time_limit=self._eval_env.max_episode_steps,
+                device=self._device,
+            )
         if self._env.need_auto_reset_wrapper:
             self._env = AutoReset(self._env, device=self._device)
             self._eval_env = AutoReset(self._eval_env, device=self._device)
@@ -192,3 +210,14 @@ def save(self) -> dict[str, torch.nn.Module]:
             The saved components of environment, e.g., ``obs_normalizer``.
         """
         return self._env.save()
+
+    def close(self) -> None:
+        """Close the environment after training."""
+        self._env.close()
+
+    @property
+    def env_spec_keys(self) -> list[str]:
+        """Return the environment specification log."""
+        if hasattr(self._env, 'env_spec_log'):
+            return list(self._env.env_spec_log.keys())
+        return []

omnisafe/adapter/onpolicy_adapter.py

@@ -158,6 +158,8 @@ def _log_metrics(self, logger: Logger, idx: int) -> None:
             logger (Logger): Logger, to log ``EpRet``, ``EpCost``, ``EpLen``.
             idx (int): The index of the environment.
         """
+        if hasattr(self._env, 'spec_log'):
+            self._env.spec_log(logger)
         logger.store(
             {
                 'Metrics/EpRet': self._ep_ret[idx],

omnisafe/algorithms/model_based/base/loop.py

@@ -305,7 +305,7 @@ def _store_real_data(  # pylint: disable=too-many-arguments,unused-argument
             info (dict[str, Any]): The information from the environment.
         """
         done = terminated or truncated
-        goal_met = False if 'goal_met' not in info else info['goal_met']
+        goal_met = info.get('goal_met', False)
         if not done and not goal_met:
             # when goal_met == true:
             # current goal position is not related to the last goal position,

omnisafe/algorithms/model_based/base/pets.py

@@ -333,7 +333,7 @@ def _update_dynamics_model(
         )
 
     def _update_epoch(self) -> None:
-        ...
+        """Update function per epoch."""
 
     def _select_action(  # pylint: disable=unused-argument
         self,
@@ -384,7 +384,7 @@ def _store_real_data(  # pylint: disable=too-many-arguments,unused-argument
             info (dict[str, Any]): The information from the environment.
         """
         done = terminated or truncated
-        goal_met = False if 'goal_met' not in info else info['goal_met']
+        goal_met = info.get('goal_met', False)
         if not terminated and not truncated and not goal_met:
             # pylint: disable-next=line-too-long
             # if goal_met == true, Current goal position is not related to the last goal position, this huge transition will confuse the dynamics model.

omnisafe/algorithms/off_policy/ddpg.py

@@ -228,6 +228,9 @@ def _init_log(self) -> None:
         self._logger.register_key('Time/Evaluate')
         self._logger.register_key('Time/Epoch')
         self._logger.register_key('Time/FPS')
+        # register environment specific keys
+        for env_spec_key in self._env.env_spec_keys:
+            self.logger.register_key(env_spec_key)
 
     def learn(self) -> tuple[float, float, float]:
         """This is main function for algorithm update.
@@ -321,6 +324,7 @@ def learn(self) -> tuple[float, float, float]:
         ep_cost = self._logger.get_stats('Metrics/EpCost')[0]
         ep_len = self._logger.get_stats('Metrics/EpLen')[0]
         self._logger.close()
+        self._env.close()
 
         return ep_ret, ep_cost, ep_len