Markov Decision Process

Markov Decision Process

• Markov Decision Process

Installation

pip install md-pro

Usage

    ##################
    ### Parameters ###
    ##################
    parser = argparse.ArgumentParser()
    parser.add_argument('--sample_time', '-Ts', type=float, help='Ts=0.1',
                        default='0.1', required=False)
    parser.add_argument('--gamma', '-gam', type=float, help='gamma=0.9',
                        default='0.9', required=False)
    parser.add_argument('--x_grid', '-xgr', type=int, help='x_grid=5',
                        default='8', required=False)
    parser.add_argument('--y_grid', '-ygr', type=int, help='y_grid=5',
                        default='5', required=False)
    parser.add_argument('--n_optimal', '-nopt', type=int, help='n_optimal=5',
                        default='5', required=False)
    args = parser.parse_args()
    params = vars(args)
    ####################################################
    ### Challenge with Markov Decision Process (MDP) ###
    ####################################################
    #start point
    strt_pnt='0'
    # points
    P=get_meshgrid_points(params)
    # Topology
    T, S = get_simple_topology_for_regular_grid(params, P)
    # rewards
    R = {'35': 100}
    mdp_challenge = {'S': S, 'R': R, 'T': T, 'P': P}

    dict_mdp=start_mdp(params, mdp_challenge)
    reach_set=reach_n_steps(strt_pnt, mdp_challenge, dict_mdp, params, steps=8)
    optimal_traj=get_trajectory(strt_pnt, dict_mdp, reach_set)
    plot_the_result(dict_mdp, mdp_challenge)

... should produce:

Citation

Please cite following document if you use this python package:

TODO

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