from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense, Activation, Flatten
from tensorflow.keras.optimizers import NAME_1
from rl.agents import DQNAgent
from rl.memory import SequentialMemory
from rl.policy import BoltzmannQPolicy, EpsGreedyQPolicy

class CustomGridEnvironment:
    def __init__(self, grid_size, partial_observability_range):
        self.grid_size = grid_size
        self.partial_observability_range = partial_observability_range
        self.observation_space_shape = (2 * partial_observability_range + 1, 2 * partial_observability_range + 1,
                                        4)  # Add 4 channels for robot positions (binary representations)

    def reset(self):
        self.robot_positions = [self._get_random_position(), self._get_random_position()]
        # return np.expand_dims(self.get_observation(), axis=0)
        return self.get_observation()

    def _get_random_position(self):
        return np.random.choice(self.grid_size[0]), np.random.choice(self.grid_size[1])

    def get_observation(self):
        observation = np.zeros(self.observation_space_shape)
        for i in range(-self.partial_observability_range, self.partial_observability_range + 1):
            for j in range(-self.partial_observability_range, self.partial_observability_range + 1):
                x1, y1 = self.robot_positions[0][0] + i, self.robot_positions[0][1] + j
                x2, y2 = self.robot_positions[1][0] + i, self.robot_positions[1][1] + j
                if 0 <= x1 < Provide your response in exactly 7 paragraphs (separated nearby paragraphs with 3 aterisk symbols ***).
user