popgym.envs.noisy_position_only_cartpole ======================================== .. py:module:: popgym.envs.noisy_position_only_cartpole Classes ------- .. autoapisummary:: popgym.envs.noisy_position_only_cartpole.NoisyPositionOnlyCartPole popgym.envs.noisy_position_only_cartpole.NoisyPositionOnlyCartPoleEasy popgym.envs.noisy_position_only_cartpole.NoisyPositionOnlyCartPoleMedium popgym.envs.noisy_position_only_cartpole.NoisyPositionOnlyCartPoleHard Module Contents --------------- .. py:class:: NoisyPositionOnlyCartPole(*args, **kwargs) Bases: :py:obj:`popgym.envs.position_only_cartpole.PositionOnlyCartPole` Position only cartpole with noise added to the observations Args: noise_sigma: The standard deviation of the noise to add to the observations **kwargs: To be passed to PositionOnlyCartPole .. py:attribute:: noise_sigma .. py:method:: step(action: gymnasium.core.ActType) -> Tuple[gymnasium.core.ObsType, float, bool, bool, dict] 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 :meth:`reset` to reset this environment's state for the next episode. .. versionchanged:: 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 :attr:`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 Barto Gridworld. If true, the user needs to call :meth:`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 :meth:`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 Tuple[gymnasium.core.ObsType, dict] 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 :meth:`reset` is called with ``seed=None``, the RNG is not reset. Therefore, :meth:`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 :meth:`reset` should be ``super().reset(seed=seed)`` which implements the seeding correctly. .. versionchanged:: 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`) and the read-only attribute `np_random_seed`. 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 and the env's :attr:`np_random_seed` will *not* be altered. 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 :attr:`observation_space` (typically a numpy array) and is analogous to the observation returned by :meth:`step`. info (dictionary): This dictionary contains auxiliary information complementing ``observation``. It should be analogous to the ``info`` returned by :meth:`step`. .. py:class:: NoisyPositionOnlyCartPoleEasy Bases: :py:obj:`NoisyPositionOnlyCartPole` Position only cartpole with noise added to the observations Args: noise_sigma: The standard deviation of the noise to add to the observations **kwargs: To be passed to PositionOnlyCartPole .. py:class:: NoisyPositionOnlyCartPoleMedium Bases: :py:obj:`NoisyPositionOnlyCartPole` Position only cartpole with noise added to the observations Args: noise_sigma: The standard deviation of the noise to add to the observations **kwargs: To be passed to PositionOnlyCartPole .. py:class:: NoisyPositionOnlyCartPoleHard Bases: :py:obj:`NoisyPositionOnlyCartPole` Position only cartpole with noise added to the observations Args: noise_sigma: The standard deviation of the noise to add to the observations **kwargs: To be passed to PositionOnlyCartPole