rlberry.envs.interface.Model

class rlberry.envs.interface.Model

Bases: Env

Base class for an environment model.

Attributes:

namestring

environment identifier

observation_spacerlberry.spaces.Space

observation space

action_spacerlberry.spaces.Space

action space

reward_rangetuple

tuple (r_min, r_max) containing the minimum and the maximum reward

seederrlberry.seeding.Seeder

Seeder, containing random number generator.

Methods

close()	After the user has finished using the environment, close contains the code necessary to "clean up" the environment.
get_params([deep])	Get parameters for this model.
get_wrapper_attr(name)	Gets the attribute name from the environment.
is_generative()	Returns true if sample() method is implemented
is_online()	Returns true if reset() and step() methods are implemented
render()	Compute the render frames as specified by render_mode during the initialization of the environment.
reseed([seed_seq])	Get new random number generator for the model.
reset(*[, seed, options])	Resets the environment to an initial internal state, returning an initial observation and info.
sample(state, action)	Execute a step from a state-action pair.
step(action)	Run one timestep of the environment's dynamics using the agent actions.

close()

After the user has finished using the environment, close contains the code necessary to “clean up” the environment.

This is critical for closing rendering windows, database or HTTP connections. Calling close on an already closed environment has no effect and won’t raise an error.

get_params(deep=True)

Get parameters for this model.

Parameters:

deepbool, default=True

If True, will return the parameters for this model and contained subobjects.

Returns:

paramsdict

Parameter names mapped to their values.

get_wrapper_attr(name: str) → Any

Gets the attribute name from the environment.

is_generative()

Returns true if sample() method is implemented

is_online()

Returns true if reset() and step() methods are implemented

property np_random: Generator

Returns the environment’s internal _np_random that if not set will initialise with a random seed.

Returns:

Instances of np.random.Generator

render() → RenderFrame | list[RenderFrame] | None

Compute the render frames as specified by render_mode during the initialization of the environment.

The environment’s metadata render modes (env.metadata[“render_modes”]) should contain the possible ways to implement the render modes. In addition, list versions for most render modes is achieved through gymnasium.make which automatically applies a wrapper to collect rendered frames.

Note:

As the render_mode is known during __init__, the objects used to render the environment state should be initialised in __init__.

By convention, if the render_mode is:

• None (default): no render is computed.

• “human”: The environment is continuously rendered in the current display or terminal, usually for human consumption. This rendering should occur during step() and render() doesn’t need to be called. Returns None.

• “rgb_array”: Return a single frame representing the current state of the environment. A frame is a np.ndarray with shape (x, y, 3) representing RGB values for an x-by-y pixel image.

• “ansi”: Return a strings (str) or StringIO.StringIO containing a terminal-style text representation for each time step. The text can include newlines and ANSI escape sequences (e.g. for colors).

• “rgb_array_list” and “ansi_list”: List based version of render modes are possible (except Human) through the wrapper, gymnasium.wrappers.RenderCollection that is automatically applied during gymnasium.make(..., render_mode="rgb_array_list"). The frames collected are popped after render() is called or reset().

Note:

Make sure that your class’s metadata "render_modes" key includes the list of supported modes.

Changed in version 0.25.0: The render function was changed to no longer accept parameters, rather these parameters should be specified in the environment initialised, i.e., gymnasium.make("CartPole-v1", render_mode="human")

reseed(seed_seq=None)

Get new random number generator for the model.

Parameters:

seed_seqnp.random.SeedSequence, rlberry.seeding.Seeder or int, defaultNone

Seed sequence from which to spawn the random number generator. If None, generate random seed. If int, use as entropy for SeedSequence. If seeder, use seeder.seed_seq

reset(*, seed: int | None = None, options: dict[str, Any] | None = None) → tuple[ObsType, dict[str, Any]]

Resets the environment to an initial internal state, returning an initial observation and info.

This method generates a new starting state often with some randomness to ensure that the agent explores the state space and learns a generalised policy about the environment. This randomness can be controlled with the seed parameter otherwise if the environment already has a random number generator and reset() is called with seed=None, the RNG is not reset.

Therefore, reset() should (in the typical use case) be called with a seed right after initialization and then never again.

For Custom environments, the first line of reset() should be super().reset(seed=seed) which implements the seeding correctly.

Changed in version v0.25: The return_info parameter was removed and now info is expected to be returned.

Args:

seed (optional int): The seed that is used to initialize the environment’s PRNG (np_random).

If the environment does not already have a PRNG and seed=None (the default option) is passed, a seed will be chosen from some source of entropy (e.g. timestamp or /dev/urandom). However, if the environment already has a PRNG and seed=None is passed, the PRNG will not be reset. If you pass an integer, the PRNG will be reset even if it already exists. Usually, you want to pass an integer right after the environment has been initialized and then never again. Please refer to the minimal example above to see this paradigm in action.

options (optional dict): Additional information to specify how the environment is reset (optional,

depending on the specific environment)

Returns:

observation (ObsType): Observation of the initial state. This will be an element of observation_space

(typically a numpy array) and is analogous to the observation returned by step().

info (dictionary): This dictionary contains auxiliary information complementing observation. It should be analogous to

the info returned by step().

property rng

Random number generator.

sample(state, action)

Execute a step from a state-action pair.

Parameters:

stateobject

state from which to sample

actionobject

action to take in the environment

Returns:

observationobject

rewardfloat

donebool

infodict

step(action: ActType) → tuple[ObsType, SupportsFloat, bool, bool, dict[str, Any]]

Run one timestep of the environment’s dynamics using the agent actions.

When the end of an episode is reached (terminated or truncated), it is necessary to call reset() to reset this environment’s state for the next episode.

Changed in version 0.26: The Step API was changed removing done in favor of terminated and truncated to make it clearer to users when the environment had terminated or truncated which is critical for reinforcement learning bootstrapping algorithms.

Args:

action (ActType): an action provided by the agent to update the environment state.

Returns:

observation (ObsType): An element of the environment’s observation_space as the next observation due to the agent actions.

An example is a numpy array containing the positions and velocities of the pole in CartPole.

reward (SupportsFloat): The reward as a result of taking the action. terminated (bool): Whether the agent reaches the terminal state (as defined under the MDP of the task)

which can be positive or negative. An example is reaching the goal state or moving into the lava from the Sutton and Barton, Gridworld. If true, the user needs to call reset().

truncated (bool): Whether the truncation condition outside the scope of the MDP is satisfied.

Typically, this is a timelimit, but could also be used to indicate an agent physically going out of bounds. Can be used to end the episode prematurely before a terminal state is reached. If true, the user needs to call reset().

info (dict): Contains auxiliary diagnostic information (helpful for debugging, learning, and logging).

This might, for instance, contain: metrics that describe the agent’s performance state, variables that are hidden from observations, or individual reward terms that are combined to produce the total reward. In OpenAI Gym <v26, it contains “TimeLimit.truncated” to distinguish truncation and termination, however this is deprecated in favour of returning terminated and truncated variables.

done (bool): (Deprecated) A boolean value for if the episode has ended, in which case further step() calls will

return undefined results. This was removed in OpenAI Gym v26 in favor of terminated and truncated attributes. A done signal may be emitted for different reasons: Maybe the task underlying the environment was solved successfully, a certain timelimit was exceeded, or the physics simulation has entered an invalid state.

property unwrapped

Returns the base non-wrapped environment.

Returns:

Env: The base non-wrapped gymnasium.Env instance