Help protect the Great Barrier Reef with TensorFlow on Kaggle Join Challenge


Loads the selected environment and wraps it with the specified wrappers.

Used in the notebooks

Used in the tutorials

Note that by default a TimeLimit wrapper is used to limit episode lengths to the default benchmarks defined by the registered environments.

environment_name Name for the environment to load.
discount Discount to use for the environment.
max_episode_steps If None the max_episode_steps will be set to the default step limit defined in the environment's spec. No limit is applied if set to 0 or if there is no max_episode_steps set in the environment's spec.
gym_env_wrappers Iterable with references to wrapper classes to use directly on the gym environment.
env_wrappers Iterable with references to wrapper classes to use on the gym_wrapped environment.
spec_dtype_map A dict that maps gym spaces to np dtypes to use as the default dtype for the arrays. An easy way how to configure a custom mapping through Gin is to define a gin-configurable function that returns desired mapping and call it in your Gin congif file, for example: suite_gym.load.spec_dtype_map = @get_custom_mapping().
gym_kwargs Optional kwargs to pass to the Gym environment class.
render_kwargs Optional kwargs for rendering to pass to render() of the gym_wrapped environment.

A PyEnvironment instance.