popgym.envs.concentration
=========================

.. py:module:: popgym.envs.concentration

.. autoapi-nested-parse::

   Classic game of concentration. A deck of cards is shuffled and placed
   face-down. The player can flip two cards, if they match they get a reward
   otherwise they dont.



Classes
-------

.. autoapisummary::

   popgym.envs.concentration.Concentration
   popgym.envs.concentration.ConcentrationEasy
   popgym.envs.concentration.ConcentrationMedium
   popgym.envs.concentration.ConcentrationHard


Module Contents
---------------

.. py:class:: Concentration(num_decks=1, deck_type='ranks')

   Bases: :py:obj:`popgym.core.env.POPGymEnv`


   Classic game of concentration. A deck of cards is shuffled and placed
   face-down. The player can flip two cards, if they match they get a reward
   otherwise they dont.

   Args:
       num_decks: Number of decks to play with
       deck_type: String denoting what we are matching. Can be the card colors
       (colors) or the card ranks (ranks)

   Returns:
       A gym environment


   .. py:attribute:: obs_requires_prev_action
      :value: True



   .. py:attribute:: deck


   .. py:attribute:: episode_length


   .. py:attribute:: success_reward_scale
      :value: 0.038461538461538464



   .. py:attribute:: failure_reward_scale


   .. py:attribute:: facedown_card


   .. py:attribute:: observation_space


   .. py:attribute:: state_space


   .. py:attribute:: action_space


   .. py:attribute:: last_in_play_idx
      :value: []



   .. py:attribute:: last_trying_card_already_up
      :value: False



   .. 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 <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 :meth:`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.



   .. py:method:: render(mode='ascii')

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

      The environment's :attr:`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 :attr:`render_mode` is known during ``__init__``, the objects used to render the environment state
          should be initialised in ``__init__``.

      By convention, if the :attr:`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 :meth:`step` and :meth:`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, :py:class:`gymnasium.wrappers.RenderCollection` that is automatically applied during ``gymnasium.make(..., render_mode="rgb_array_list")``.
        The frames collected are popped after :meth:`render` is called or :meth:`reset`.

      Note:
          Make sure that your class's :attr:`metadata` ``"render_modes"`` key includes the list of supported modes.

      .. versionchanged:: 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")``



   .. py:method:: get_obs()


   .. py:method:: get_state()

      Returns the underlying hidden Markov state



   .. py:method:: reset(*, seed: Optional[int] = None, options: Optional[dict] = None) -> Tuple[gymnasium.core.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
      :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:: ConcentrationEasy

   Bases: :py:obj:`Concentration`


   Classic game of concentration. A deck of cards is shuffled and placed
   face-down. The player can flip two cards, if they match they get a reward
   otherwise they dont.

   Args:
       num_decks: Number of decks to play with
       deck_type: String denoting what we are matching. Can be the card colors
       (colors) or the card ranks (ranks)

   Returns:
       A gym environment


.. py:class:: ConcentrationMedium

   Bases: :py:obj:`Concentration`


   Classic game of concentration. A deck of cards is shuffled and placed
   face-down. The player can flip two cards, if they match they get a reward
   otherwise they dont.

   Args:
       num_decks: Number of decks to play with
       deck_type: String denoting what we are matching. Can be the card colors
       (colors) or the card ranks (ranks)

   Returns:
       A gym environment


.. py:class:: ConcentrationHard

   Bases: :py:obj:`Concentration`


   Classic game of concentration. A deck of cards is shuffled and placed
   face-down. The player can flip two cards, if they match they get a reward
   otherwise they dont.

   Args:
       num_decks: Number of decks to play with
       deck_type: String denoting what we are matching. Can be the card colors
       (colors) or the card ranks (ranks)

   Returns:
       A gym environment