def soft_policy(states, nr_actions):
    """
    Define behaviour policy of off-policy MC method as soft policy with 
    uniform probability distribution

    Parameters
    ----------
    states : list of tuples
        States consist of positions and velocity of agent
    nr_actions : int
        number of policy actions of agent in environment

    Returns
    -------
    b : dict
        Dictionary representing the policy  which maps states on actions. Each 
        action has the same probability of 1/nr_actions. Therefore 
        b(action|state) is defined as b[state] =  and utilized correspondingly
        in the off-policy MC algorithm

    """
    b = {}
    for pos in states[0]:
        for vel in states[1]:
            b[pos, vel] = list(range(0,nr_actions))
    
    return b