training_classical_control.environment

`training_classical_control.environment`#

Module Contents#

Classes#

SimulationResults

Functions#

`create_inverted_pendulum_environment`	Creates instance of InvertedPendulumEnv with some wrappers to ensure correctness, limit the number of steps and store rendered frames.
`simulate_environment`

Data#

__all__

API#

training_classical_control.environment.__all__#: [‘create_inverted_pendulum_environment’, ‘simulate_environment’]

training_classical_control.environment.create_inverted_pendulum_environment(render_mode: str | None = 'rgb_array', *, max_steps: int = 500, masspole: float = 0.1, masscart: float = 1.0, length: float = 1.0, x_threshold: float = 3, theta_threshold: float = 24, force_max: float = 10.0) → gymnasium.Env[source]#

Creates instance of InvertedPendulumEnv with some wrappers to ensure correctness, limit the number of steps and store rendered frames.

Args: render_mode: Render mode for environment. max_steps: Maximum number of steps in the environment before termination. masspole: mass of the pole. masscart: mass of the cart. length: length of the pole. force_max: maximum absolute value for force applied to Cart. x_threshold: Threshold value for cart position. theta_threshold: Threshold value for pole angle.

Returns: Instantiated and wrapped environment.

class training_classical_control.environment.SimulationResults[source]#

frames: list[numpy.typing.NDArray]#: None

observations: numpy.typing.NDArray#: None

estimated_observations: numpy.typing.NDArray#: None

actions: numpy.typing.NDArray#: None

training_classical_control.environment.simulate_environment(env: gymnasium.Env, *, max_steps: int = 500, controller: training_classical_control.control.FeedbackController | None = None, observer: training_classical_control.control.Observer | None = None) → training_classical_control.environment.SimulationResults[source]#