import os
import random
import numpy as np
from matplotlib.image import imread


class MarsRover:
    """
    An MDP whose labels depend on a background image

    ...

    Attributes
    ----------
    image: string
        path to a layout file

    Methods
    -------
    reset()
        resets the MDP state, including ghosts and agent
    step(action)
        changes the state of the MDP upon executing an action, where the action set is {right,up,left,down,stay}
    state_label(state)
        outputs the label of input state
    """

    def __init__(
            self,
            image='marsrover_1.png',
            initial_state=None
    ):
        self.background = imread(os.path.join(os.path.dirname(os.path.abspath(__file__)), 'layouts', image))
        self.width = self.background.shape[0]
        self.height = self.background.shape[1]
        self.labels = self.background.__array__()
        self.initial_state = initial_state
        if self.initial_state is None:
            self.initial_state = np.array([35, 25], dtype=np.float32)
        self.current_state = self.initial_state
        # directional actions
        self.action_space = [
            "right",
            "up",
            "left",
            "down",
            "stay"
        ]

    def reset(self):
        self.current_state = self.initial_state.copy()

    def step(self, action):
        # agent movement dynamics:
        # # stochasticity
        traversed_distance = 2
        noise = np.array([random.uniform(-0.1, 0.5), random.uniform(-0.1, 0.5)])
        if action == 'right':
            next_state = self.current_state + [0, traversed_distance] + noise
        elif action == 'up':
            next_state = self.current_state + [-traversed_distance, 0] + noise
        elif action == 'left':
            next_state = self.current_state + [0, -traversed_distance] + noise
        elif action == 'down':
            next_state = self.current_state + [traversed_distance, 0] + noise
        elif action == 'stay':
            next_state = self.current_state + [0, 0] + noise

        # check for boundary violations
        if next_state[0] > self.width-1:
            next_state[0] = self.width-1
        if next_state[1] > self.height-1:
            next_state[1] = self.height-1
        if next_state[0] < 0:
            next_state[0] = 0
        if next_state[1] < 0:
            next_state[1] = 0

        # update current state
        self.current_state = next_state
        return next_state

    def state_label(self, state):
        # note: labels are inevitably discrete when reading an image file
        # thus in the following we look where the continuous state lies within
        # the image rgb matrix
        low_bound = [abs(state[i]-int(state[i])) for i in range(len(state))]
        high_bound = [1-low_bound[i] for i in range(len(state))]
        state_rgb_indx = []
        for i in range(len(state)):
            if low_bound[i] <= high_bound[i]:
                # check for boundary
                if int(state[i]) > self.background.shape[i] - 1:
                    state_rgb_indx.append(self.background.shape[i] - 1)
                else:
                    state_rgb_indx.append(int(state[i]))
            else:
                # check for boundary
                if int(state[i]) + 1 > self.background.shape[i] - 1:
                    state_rgb_indx.append(self.background.shape[i] - 1)
                else:
                    state_rgb_indx.append(int(state[i])+1)

        if list(self.labels[state_rgb_indx[0], state_rgb_indx[1]]) == list(self.labels[0, 199]) or \
                list(self.labels[state_rgb_indx[0], state_rgb_indx[1]]) == list(self.labels[144, 199]):
            return 'unsafe'
        elif list(self.labels[state_rgb_indx[0], state_rgb_indx[1]]) == list(self.labels[45, 152]):
            return 'goal1'
        elif list(self.labels[state_rgb_indx[0], state_rgb_indx[1]]) == list(self.labels[62, 16]):
            return 'goal2'
        else:
            return 'safe'


4b63e6d58b4c54012c6414aa53f13c3b3afa6344
<mask token>


class MarsRover:
    <mask token>

    def __init__(self, image='marsrover_1.png', initial_state=None):
        self.background = imread(os.path.join(os.path.dirname(os.path.
            abspath(__file__)), 'layouts', image))
        self.width = self.background.shape[0]
        self.height = self.background.shape[1]
        self.labels = self.background.__array__()
        self.initial_state = initial_state
        if self.initial_state is None:
            self.initial_state = np.array([35, 25], dtype=np.float32)
        self.current_state = self.initial_state
        self.action_space = ['right', 'up', 'left', 'down', 'stay']

    def reset(self):
        self.current_state = self.initial_state.copy()

    def step(self, action):
        traversed_distance = 2
        noise = np.array([random.uniform(-0.1, 0.5), random.uniform(-0.1, 0.5)]
            )
        if action == 'right':
            next_state = self.current_state + [0, traversed_distance] + noise
        elif action == 'up':
            next_state = self.current_state + [-traversed_distance, 0] + noise
        elif action == 'left':
            next_state = self.current_state + [0, -traversed_distance] + noise
        elif action == 'down':
            next_state = self.current_state + [traversed_distance, 0] + noise
        elif action == 'stay':
            next_state = self.current_state + [0, 0] + noise
        if next_state[0] > self.width - 1:
            next_state[0] = self.width - 1
        if next_state[1] > self.height - 1:
            next_state[1] = self.height - 1
        if next_state[0] < 0:
            next_state[0] = 0
        if next_state[1] < 0:
            next_state[1] = 0
        self.current_state = next_state
        return next_state
    <mask token>

<mask token>


class MarsRover:
    <mask token>

    def __init__(self, image='marsrover_1.png', initial_state=None):
        self.background = imread(os.path.join(os.path.dirname(os.path.
            abspath(__file__)), 'layouts', image))
        self.width = self.background.shape[0]
        self.height = self.background.shape[1]
        self.labels = self.background.__array__()
        self.initial_state = initial_state
        if self.initial_state is None:
            self.initial_state = np.array([35, 25], dtype=np.float32)
        self.current_state = self.initial_state
        self.action_space = ['right', 'up', 'left', 'down', 'stay']

    def reset(self):
        self.current_state = self.initial_state.copy()

    def step(self, action):
        traversed_distance = 2
        noise = np.array([random.uniform(-0.1, 0.5), random.uniform(-0.1, 0.5)]
            )
        if action == 'right':
            next_state = self.current_state + [0, traversed_distance] + noise
        elif action == 'up':
            next_state = self.current_state + [-traversed_distance, 0] + noise
        elif action == 'left':
            next_state = self.current_state + [0, -traversed_distance] + noise
        elif action == 'down':
            next_state = self.current_state + [traversed_distance, 0] + noise
        elif action == 'stay':
            next_state = self.current_state + [0, 0] + noise
        if next_state[0] > self.width - 1:
            next_state[0] = self.width - 1
        if next_state[1] > self.height - 1:
            next_state[1] = self.height - 1
        if next_state[0] < 0:
            next_state[0] = 0
        if next_state[1] < 0:
            next_state[1] = 0
        self.current_state = next_state
        return next_state

    def state_label(self, state):
        low_bound = [abs(state[i] - int(state[i])) for i in range(len(state))]
        high_bound = [(1 - low_bound[i]) for i in range(len(state))]
        state_rgb_indx = []
        for i in range(len(state)):
            if low_bound[i] <= high_bound[i]:
                if int(state[i]) > self.background.shape[i] - 1:
                    state_rgb_indx.append(self.background.shape[i] - 1)
                else:
                    state_rgb_indx.append(int(state[i]))
            elif int(state[i]) + 1 > self.background.shape[i] - 1:
                state_rgb_indx.append(self.background.shape[i] - 1)
            else:
                state_rgb_indx.append(int(state[i]) + 1)
        if list(self.labels[state_rgb_indx[0], state_rgb_indx[1]]) == list(self
            .labels[0, 199]) or list(self.labels[state_rgb_indx[0],
            state_rgb_indx[1]]) == list(self.labels[144, 199]):
            return 'unsafe'
        elif list(self.labels[state_rgb_indx[0], state_rgb_indx[1]]) == list(
            self.labels[45, 152]):
            return 'goal1'
        elif list(self.labels[state_rgb_indx[0], state_rgb_indx[1]]) == list(
            self.labels[62, 16]):
            return 'goal2'
        else:
            return 'safe'

<mask token>


class MarsRover:
    """
    An MDP whose labels depend on a background image

    ...

    Attributes
    ----------
    image: string
        path to a layout file

    Methods
    -------
    reset()
        resets the MDP state, including ghosts and agent
    step(action)
        changes the state of the MDP upon executing an action, where the action set is {right,up,left,down,stay}
    state_label(state)
        outputs the label of input state
    """

    def __init__(self, image='marsrover_1.png', initial_state=None):
        self.background = imread(os.path.join(os.path.dirname(os.path.
            abspath(__file__)), 'layouts', image))
        self.width = self.background.shape[0]
        self.height = self.background.shape[1]
        self.labels = self.background.__array__()
        self.initial_state = initial_state
        if self.initial_state is None:
            self.initial_state = np.array([35, 25], dtype=np.float32)
        self.current_state = self.initial_state
        self.action_space = ['right', 'up', 'left', 'down', 'stay']

    def reset(self):
        self.current_state = self.initial_state.copy()

    def step(self, action):
        traversed_distance = 2
        noise = np.array([random.uniform(-0.1, 0.5), random.uniform(-0.1, 0.5)]
            )
        if action == 'right':
            next_state = self.current_state + [0, traversed_distance] + noise
        elif action == 'up':
            next_state = self.current_state + [-traversed_distance, 0] + noise
        elif action == 'left':
            next_state = self.current_state + [0, -traversed_distance] + noise
        elif action == 'down':
            next_state = self.current_state + [traversed_distance, 0] + noise
        elif action == 'stay':
            next_state = self.current_state + [0, 0] + noise
        if next_state[0] > self.width - 1:
            next_state[0] = self.width - 1
        if next_state[1] > self.height - 1:
            next_state[1] = self.height - 1
        if next_state[0] < 0:
            next_state[0] = 0
        if next_state[1] < 0:
            next_state[1] = 0
        self.current_state = next_state
        return next_state

    def state_label(self, state):
        low_bound = [abs(state[i] - int(state[i])) for i in range(len(state))]
        high_bound = [(1 - low_bound[i]) for i in range(len(state))]
        state_rgb_indx = []
        for i in range(len(state)):
            if low_bound[i] <= high_bound[i]:
                if int(state[i]) > self.background.shape[i] - 1:
                    state_rgb_indx.append(self.background.shape[i] - 1)
                else:
                    state_rgb_indx.append(int(state[i]))
            elif int(state[i]) + 1 > self.background.shape[i] - 1:
                state_rgb_indx.append(self.background.shape[i] - 1)
            else:
                state_rgb_indx.append(int(state[i]) + 1)
        if list(self.labels[state_rgb_indx[0], state_rgb_indx[1]]) == list(self
            .labels[0, 199]) or list(self.labels[state_rgb_indx[0],
            state_rgb_indx[1]]) == list(self.labels[144, 199]):
            return 'unsafe'
        elif list(self.labels[state_rgb_indx[0], state_rgb_indx[1]]) == list(
            self.labels[45, 152]):
            return 'goal1'
        elif list(self.labels[state_rgb_indx[0], state_rgb_indx[1]]) == list(
            self.labels[62, 16]):
            return 'goal2'
        else:
            return 'safe'

import os
import random
import numpy as np
from matplotlib.image import imread


class MarsRover:
    """
    An MDP whose labels depend on a background image

    ...

    Attributes
    ----------
    image: string
        path to a layout file

    Methods
    -------
    reset()
        resets the MDP state, including ghosts and agent
    step(action)
        changes the state of the MDP upon executing an action, where the action set is {right,up,left,down,stay}
    state_label(state)
        outputs the label of input state
    """

    def __init__(self, image='marsrover_1.png', initial_state=None):
        self.background = imread(os.path.join(os.path.dirname(os.path.
            abspath(__file__)), 'layouts', image))
        self.width = self.background.shape[0]
        self.height = self.background.shape[1]
        self.labels = self.background.__array__()
        self.initial_state = initial_state
        if self.initial_state is None:
            self.initial_state = np.array([35, 25], dtype=np.float32)
        self.current_state = self.initial_state
        self.action_space = ['right', 'up', 'left', 'down', 'stay']

    def reset(self):
        self.current_state = self.initial_state.copy()

    def step(self, action):
        traversed_distance = 2
        noise = np.array([random.uniform(-0.1, 0.5), random.uniform(-0.1, 0.5)]
            )
        if action == 'right':
            next_state = self.current_state + [0, traversed_distance] + noise
        elif action == 'up':
            next_state = self.current_state + [-traversed_distance, 0] + noise
        elif action == 'left':
            next_state = self.current_state + [0, -traversed_distance] + noise
        elif action == 'down':
            next_state = self.current_state + [traversed_distance, 0] + noise
        elif action == 'stay':
            next_state = self.current_state + [0, 0] + noise
        if next_state[0] > self.width - 1:
            next_state[0] = self.width - 1
        if next_state[1] > self.height - 1:
            next_state[1] = self.height - 1
        if next_state[0] < 0:
            next_state[0] = 0
        if next_state[1] < 0:
            next_state[1] = 0
        self.current_state = next_state
        return next_state

    def state_label(self, state):
        low_bound = [abs(state[i] - int(state[i])) for i in range(len(state))]
        high_bound = [(1 - low_bound[i]) for i in range(len(state))]
        state_rgb_indx = []
        for i in range(len(state)):
            if low_bound[i] <= high_bound[i]:
                if int(state[i]) > self.background.shape[i] - 1:
                    state_rgb_indx.append(self.background.shape[i] - 1)
                else:
                    state_rgb_indx.append(int(state[i]))
            elif int(state[i]) + 1 > self.background.shape[i] - 1:
                state_rgb_indx.append(self.background.shape[i] - 1)
            else:
                state_rgb_indx.append(int(state[i]) + 1)
        if list(self.labels[state_rgb_indx[0], state_rgb_indx[1]]) == list(self
            .labels[0, 199]) or list(self.labels[state_rgb_indx[0],
            state_rgb_indx[1]]) == list(self.labels[144, 199]):
            return 'unsafe'
        elif list(self.labels[state_rgb_indx[0], state_rgb_indx[1]]) == list(
            self.labels[45, 152]):
            return 'goal1'
        elif list(self.labels[state_rgb_indx[0], state_rgb_indx[1]]) == list(
            self.labels[62, 16]):
            return 'goal2'
        else:
            return 'safe'

import os
import random
import numpy as np
from matplotlib.image import imread


class MarsRover:
    """
    An MDP whose labels depend on a background image

    ...

    Attributes
    ----------
    image: string
        path to a layout file

    Methods
    -------
    reset()
        resets the MDP state, including ghosts and agent
    step(action)
        changes the state of the MDP upon executing an action, where the action set is {right,up,left,down,stay}
    state_label(state)
        outputs the label of input state
    """

    def __init__(
            self,
            image='marsrover_1.png',
            initial_state=None
    ):
        self.background = imread(os.path.join(os.path.dirname(os.path.abspath(__file__)), 'layouts', image))
        self.width = self.background.shape[0]
        self.height = self.background.shape[1]
        self.labels = self.background.__array__()
        self.initial_state = initial_state
        if self.initial_state is None:
            self.initial_state = np.array([35, 25], dtype=np.float32)
        self.current_state = self.initial_state
        # directional actions
        self.action_space = [
            "right",
            "up",
            "left",
            "down",
            "stay"
        ]

    def reset(self):
        self.current_state = self.initial_state.copy()

    def step(self, action):
        # agent movement dynamics:
        # # stochasticity
        traversed_distance = 2
        noise = np.array([random.uniform(-0.1, 0.5), random.uniform(-0.1, 0.5)])
        if action == 'right':
            next_state = self.current_state + [0, traversed_distance] + noise
        elif action == 'up':
            next_state = self.current_state + [-traversed_distance, 0] + noise
        elif action == 'left':
            next_state = self.current_state + [0, -traversed_distance] + noise
        elif action == 'down':
            next_state = self.current_state + [traversed_distance, 0] + noise
        elif action == 'stay':
            next_state = self.current_state + [0, 0] + noise

        # check for boundary violations
        if next_state[0] > self.width-1:
            next_state[0] = self.width-1
        if next_state[1] > self.height-1:
            next_state[1] = self.height-1
        if next_state[0] < 0:
            next_state[0] = 0
        if next_state[1] < 0:
            next_state[1] = 0

        # update current state
        self.current_state = next_state
        return next_state

    def state_label(self, state):
        # note: labels are inevitably discrete when reading an image file
        # thus in the following we look where the continuous state lies within
        # the image rgb matrix
        low_bound = [abs(state[i]-int(state[i])) for i in range(len(state))]
        high_bound = [1-low_bound[i] for i in range(len(state))]
        state_rgb_indx = []
        for i in range(len(state)):
            if low_bound[i] <= high_bound[i]:
                # check for boundary
                if int(state[i]) > self.background.shape[i] - 1:
                    state_rgb_indx.append(self.background.shape[i] - 1)
                else:
                    state_rgb_indx.append(int(state[i]))
            else:
                # check for boundary
                if int(state[i]) + 1 > self.background.shape[i] - 1:
                    state_rgb_indx.append(self.background.shape[i] - 1)
                else:
                    state_rgb_indx.append(int(state[i])+1)

        if list(self.labels[state_rgb_indx[0], state_rgb_indx[1]]) == list(self.labels[0, 199]) or \
                list(self.labels[state_rgb_indx[0], state_rgb_indx[1]]) == list(self.labels[144, 199]):
            return 'unsafe'
        elif list(self.labels[state_rgb_indx[0], state_rgb_indx[1]]) == list(self.labels[45, 152]):
            return 'goal1'
        elif list(self.labels[state_rgb_indx[0], state_rgb_indx[1]]) == list(self.labels[62, 16]):
            return 'goal2'
        else:
            return 'safe'

