scml.std.rl.observation
=======================

.. py:module:: scml.std.rl.observation


Attributes
----------

.. autoapisummary::

   scml.std.rl.observation.DefaultObservationManager


Classes
-------

.. autoapisummary::

   scml.std.rl.observation.FlexibleObservationManager
   scml.std.rl.observation.ObservationManager


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

.. py:data:: DefaultObservationManager

   The default observation manager

.. py:class:: FlexibleObservationManager

   Bases: :py:obj:`BaseObservationManager`


   An observation manager that can be used with any SCML world.

   :param capacity_multiplier: A factor to multiply by the number of lines to give the maximum quantity allowed in offers
   :param exogenous_multiplier: A factor to multiply maximum production capacity with when encoding exogenous quantities
   :param continuous: If given the observation space will be a Box otherwise it will be a MultiDiscrete
   :param n_prices: The number of prices to use for encoding the unit price (if not `continuous`)
   :param max_production_cost: The limit for production cost. Anything above that will be mapped to this max
   :param max_group_size: Maximum size used for grouping observations from multiple partners. This will be
                          used in the number of partners in the simulation is larger than the number used
                          for training.
   :param n_past_received_offers: Number of past received offers to add to the observation.
   :param n_bins:
                  N. bins to use for discretization (if not `continuous`)
   :param n_sigmas: The number of sigmas used for limiting the range of randomly distributed variables
   :param extra_checks: If given, extra checks are applied to make sure encoding and decoding make sense

   Remarks:
       ...


   .. py:attribute:: capacity_multiplier
      :type:  int
      :value: 1



   .. py:attribute:: n_prices
      :type:  int
      :value: 2



   .. py:attribute:: max_group_size
      :type:  int
      :value: 2



   .. py:attribute:: reduce_space_size
      :type:  bool
      :value: True



   .. py:attribute:: n_past_received_offers
      :type:  int
      :value: 1



   .. py:attribute:: extra_checks
      :type:  bool
      :value: False



   .. py:attribute:: n_bins
      :type:  int
      :value: 40



   .. py:attribute:: n_sigmas
      :type:  int
      :value: 2



   .. py:attribute:: max_production_cost
      :type:  int
      :value: 10



   .. py:attribute:: exogenous_multiplier
      :type:  int
      :value: 1



   .. py:attribute:: max_quantity
      :type:  int


   .. py:attribute:: _chosen_partner_indices
      :type:  list[int] | None


   .. py:attribute:: _previous_offers
      :type:  collections.deque


   .. py:attribute:: _dims
      :type:  list[int] | None


   .. py:method:: __attrs_post_init__()


   .. py:method:: get_dims() -> list[int]

      Get the sizes of all dimensions in the observation space. Used if not continuous.



   .. py:method:: make_space() -> gymnasium.spaces.MultiDiscrete | gymnasium.spaces.Box

      Creates the action space



   .. py:method:: make_first_observation(awi: scml.oneshot.awi.OneShotAWI) -> numpy.ndarray

      Creates the initial observation (returned from gym's reset())



   .. py:method:: encode(awi: scml.oneshot.awi.OneShotAWI) -> numpy.ndarray

      Encodes the awi as an array



   .. py:method:: extra_obs(awi: scml.oneshot.awi.OneShotAWI) -> list[tuple[float, int] | float]

      The observation values other than offers and previous offers.

      :returns: A list of tuples. Each is some observation variable as a
                real number between zero and one and a number of bins to
                use for discrediting this variable. If a single value, the
                number of bins will be self.n_bin



   .. py:method:: get_offers(awi: scml.oneshot.awi.OneShotAWI, encoded: numpy.ndarray) -> dict[str, negmas.outcomes.Outcome | None]

      Gets offers from an encoded awi.



.. py:class:: ObservationManager

   Bases: :py:obj:`Protocol`


   Manages the observations of an agent in an RL environment


   .. py:property:: context
      :type: scml.oneshot.context.BaseContext



   .. py:method:: make_space() -> gymnasium.spaces.Space

      Creates the observation space



   .. py:method:: encode(awi: scml.oneshot.awi.OneShotAWI) -> numpy.ndarray

      Encodes an observation from the agent's awi



   .. py:method:: make_first_observation(awi: scml.oneshot.awi.OneShotAWI) -> numpy.ndarray

      Creates the initial observation (returned from gym's reset())



   .. py:method:: get_offers(awi: scml.oneshot.awi.OneShotAWI, encoded: numpy.ndarray) -> dict[str, negmas.outcomes.Outcome | None]

      Gets the offers from an encoded awi