stable-baselines3/.github/ISSUE_TEMPLATE/custom_env.yml

name: "\U0001F916 Custom Gym Environment Issue"
description: How to report an issue when using a custom Gym environment
labels: ["question", "custom gym env"]
body:
  - type: markdown
    attributes:
      value: |
        **Important Note: We do not do technical support, nor consulting** and don't answer personal questions per email.
        Please post your question on the [RL Discord](https://discord.com/invite/xhfNqQv), [Reddit](https://www.reddit.com/r/reinforcementlearning/) or [Stack Overflow](https://stackoverflow.com/) in that case.

        **Please check your environment first using**:
        ```python
        from stable_baselines3.common.env_checker import check_env

        env = CustomEnv(arg1, ...)
        # It will check your custom environment and output additional warnings if needed
        check_env(env)
        ```        
  - type: textarea
    id: description
    attributes:
      label: 🐛 Bug
      description: A clear and concise description of what the bug is.
    validations:
      required: true
  - type: textarea
    id: code-example
    attributes:
      label: Code example
      description: |
        Please try to provide a minimal example to reproduce the bug.
        For a custom environment, you need to give at least the observation space, action space, `reset()` and `step()` methods (see working example below).
        Error messages and stack traces are also helpful.
        Please use the [markdown code blocks](https://help.github.com/en/articles/creating-and-highlighting-code-blocks) for both code and stack traces.        
      value: |
        ```python
        import gym
        import numpy as np
        from gym import spaces

        from stable_baselines3 import A2C
        from stable_baselines3.common.env_checker import check_env


        class CustomEnv(gym.Env):

            def __init__(self):
                super().__init__()
                self.observation_space = spaces.Box(low=-np.inf, high=np.inf, shape=(14,))
                self.action_space = spaces.Box(low=-1, high=1, shape=(6,))

            def reset(self, seed=None):
                return self.observation_space.sample(), {}

            def step(self, action):
                obs = self.observation_space.sample()
                reward = 1.0
                terminated = False
                truncated = False
                info = {}
                return obs, reward, terminated, truncated, info

        env = CustomEnv()
        check_env(env)

        model = A2C("MlpPolicy", env, verbose=1).learn(1000)
        ```        

  - type: textarea
    id: traceback
    attributes:
      label: Relevant log output / Error message
      description: Please copy and paste any relevant log output / error message. This will be automatically formatted into code, so no need for backticks.
      placeholder: "Traceback (most recent call last): File ..."
      render: shell

  - type: textarea
    id: system-info
    attributes:
      label: System Info
      description: |
        Describe the characteristic of your environment:
         * Describe how the library was installed (pip, docker, source, ...)
         * GPU models and configuration
         * Python version
         * PyTorch version
         * Gym version
         * Versions of any other relevant libraries

        You can use `sb3.get_system_info()` to print relevant packages info:
        ```sh
        python -c 'import stable_baselines3 as sb3; sb3.get_system_info()'
        ```        
  - type: checkboxes
    id: terms
    attributes:
      label: Checklist
      options:
        - label: I have checked that there is no similar [issue](https://github.com/DLR-RM/stable-baselines3/issues) in the repo
          required: true
        - label: I have read the [documentation](https://stable-baselines3.readthedocs.io/en/master/)
          required: true
        - label: I have provided a minimal working example to reproduce the bug
          required: true
        - label: I have checked my env using the env checker
          required: true
        - label: I've used the [markdown code blocks](https://help.github.com/en/articles/creating-and-highlighting-code-blocks) for both code and stack traces.
          required: true