tf_agents.environments.suite_gym.wrap_env

Wraps given gym environment with TF Agent's GymWrapper.

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

gym_env An instance of OpenAI gym environment.
discount Discount to use for the environment.
max_episode_steps Used to create a TimeLimitWrapper. No limit is applied if set to None or 0. Usually set to gym_spec.max_episode_steps in load. </td> </tr><tr> <td>gym_env_wrappers</td> <td> Iterable with references to wrapper classes to use directly on the gym environment. </td> </tr><tr> <td>time_limit_wrapper</td> <td> Wrapper that accepts (env, max_episode_steps) params to enforce a TimeLimit. Usuaully this should be left as the default, wrappers.TimeLimit. </td> </tr><tr> <td>env_wrappers</td> <td> Iterable with references to wrapper classes to use on the gym_wrapped environment. </td> </tr><tr> <td>spec_dtype_map</td> <td> A dict that maps gym specs to tf dtypes to use as the default dtype for the tensors. 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 config file, for example:suite_gym.load.spec_dtype_map = @get_custom_mapping(). </td> </tr><tr> <td>auto_reset</td> <td> If True (default), reset the environment automatically after a terminal state is reached. </td> </tr><tr> <td>render_kwargs</td> <td> Optionaldict` of keywoard arguments for rendering.

A PyEnvironment instance.