garage.envs.garage_env module

Wrapper class that converts gym.Env into GarageEnv.

class GarageEnv(env=None, env_name='', is_image=False)

Bases: gym.core.Wrapper

Returns an abstract Garage wrapper class for gym.Env.

In order to provide pickling (serialization) and parameterization for gym.Envs, they must be wrapped with a GarageEnv. This ensures compatibility with existing samplers and checkpointing when the envs are passed internally around garage.

Furthermore, classes inheriting from GarageEnv should silently convert action_space and observation_space from gym.Spaces to akro.spaces.

Parameters:

• env (gym.Env) – An env that will be wrapped
• env_name (str) – If the env_name is speficied, a gym environment with that name will be created. If such an environment does not exist, a gym.error is thrown.
• is_image (bool) – True if observations contain pixel values, false otherwise. Setting this to true converts a gym.Spaces.Box obs space to an akro.Image and normalizes pixel values.

close()

Close the wrapped env.

reset(**kwargs)

Call reset on wrapped env.

This method is necessary to suppress a deprecated warning thrown by gym.Wrapper.

Parameters:kwargs – Keyword args Returns:The initial observation. Return type:object

spec

Return the environment specification.

This property needs to exist, since it’s defined as a property in gym.Wrapper in a way that makes it difficult to overwrite.

Returns:The envionrment specification. Return type:garage.envs.env_spec.EnvSpec

step(action)

Call step on wrapped env.

This method is necessary to suppress a deprecated warning thrown by gym.Wrapper.

Parameters:action (object) – An action provided by the agent. Returns:Agent’s observation of the current environment float : Amount of reward returned after previous action bool : Whether the episode has ended, in which case further step()

calls will return undefined results

dict: Contains auxiliary diagnostic information (helpful for

debugging, and sometimes learning)

Return type:object