# rl_coach.filters.action.attention_discretization — Reinforcement Learning Coach 0.12.0 documentation

# Source code for rl_coach.filters.action.attention_discretization

    #
    # Copyright (c) 2017 Intel Corporation
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    # Licensed under the Apache License, Version 2.0 (the "License");
    # you may not use this file except in compliance with the License.
    # You may obtain a copy of the License at
    #
    #      http://www.apache.org/licenses/LICENSE-2.0
    #
    # Unless required by applicable law or agreed to in writing, software
    # distributed under the License is distributed on an "AS IS" BASIS,
    # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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    from typing import Union, List

    import numpy as np

    from rl_coach.filters.action.box_discretization import BoxDiscretization
    from rl_coach.filters.action.partial_discrete_action_space_map import PartialDiscreteActionSpaceMap
    from rl_coach.spaces import AttentionActionSpace, BoxActionSpace, DiscreteActionSpace

    class AttentionDiscretization(PartialDiscreteActionSpaceMap):
        """
        Discretizes an **AttentionActionSpace**. The attention action space defines the actions
        as choosing sub-boxes in a given box. For example, consider an image of size 100x100, where the action is choosing
        a crop window of size 20x20 to attend to in the image. AttentionDiscretization allows discretizing the possible crop
        windows to choose into a finite number of options, and map a discrete action space into those crop windows.

        Warning! this will currently only work for attention spaces with 2 dimensions.
        """
        def __init__(self, num_bins_per_dimension: Union[int, List[int]], force_int_bins=False):
            """
            :param num_bins_per_dimension: Number of discrete bins to use for each dimension of the action space
            :param force_int_bins: If set to True, all the bins will represent integer coordinates in space.
            """
            # we allow specifying either a single number for all dimensions, or a single number per dimension in the target
            # action space
            self.num_bins_per_dimension = num_bins_per_dimension

            self.force_int_bins = force_int_bins

            # TODO: this will currently only work for attention spaces with 2 dimensions. generalize it.

            super().__init__()

        def validate_output_action_space(self, output_action_space: AttentionActionSpace):
            if not isinstance(output_action_space, AttentionActionSpace):
                raise ValueError("AttentionActionSpace discretization only works with an output space of type AttentionActionSpace. "
                                 "The given output space is {}".format(output_action_space))

        def get_unfiltered_action_space(self, output_action_space: AttentionActionSpace) -> DiscreteActionSpace:
            if isinstance(self.num_bins_per_dimension, int):
                self.num_bins_per_dimension = [self.num_bins_per_dimension] * output_action_space.shape[0]

            # create a discrete to linspace map to ease the extraction of attention actions
            discrete_to_box = BoxDiscretization([n+1 for n in self.num_bins_per_dimension],
                                                self.force_int_bins)
            discrete_to_box.get_unfiltered_action_space(BoxActionSpace(output_action_space.shape,
                                                                       output_action_space.low,
                                                                       output_action_space.high), )

            rows, cols = self.num_bins_per_dimension
            start_ind = [i * (cols + 1) + j for i in range(rows + 1) if i < rows for j in range(cols + 1) if j < cols]
            end_ind = [i + cols + 2 for i in start_ind]
            self.target_actions = [np.array([discrete_to_box.target_actions[start],
                                             discrete_to_box.target_actions[end]])
                                   for start, end in zip(start_ind, end_ind)]

            return super().get_unfiltered_action_space(output_action_space)