import numpy as np

def select_important_state():
    """
    Returns the indices of the most important state dimensions for coordinated multi-agent food collection in LBF.
    - Food layer (field): Indices [111-118], [121-128], ..., [181-188] (locations of food, i.e., reward sources)
    - Agent layer (field): Indices [11-18], [21-28], ..., [81-88] (locations of agents, needed for coordination)
    - Access layer (field): Indices [211-218], [221-228], ..., [281-288] (locations accessible, useful for path planning)
    
    Food and agent field indices are prioritized as they're dynamic and critical for coordination and reward.
    Access field indices are included but less critical if the map is static.
    Padding indices are excluded as they do not impact the task.
    """
    important_dims = []

    # Agent layer (field): s[11-18], s[21-28], ..., s[81-88]
    for row in range(1, 9):  # rows 1 to 8 (inclusive)
        important_dims.extend(range(row*10 + 1, row*10 + 9))  # +9 is exclusive

    # Food layer (field): s[111-118], s[121-128], ..., s[181-188]
    for row in range(1, 9):
        important_dims.extend(range(100 + row*10 + 1, 100 + row*10 + 9))

    # Access layer (field): s[211-218], s[221-228], ..., s[281-288]
    for row in range(1, 9):
        important_dims.extend(range(200 + row*10 + 1, 200 + row*10 + 9))

    # If you want to prioritize, you could return in order: food, agent, access.
    # For now, we return all important field indices.
    return important_dims
