def get_mdp_refined(self) -> MDPRefined: return MDPRefined(self.get_mdp_refined_dict(), gamma=1.)
1: (0.2, 4.8), 2: (0.4, 9.2), 3: (0.4, -8.2) } }, 3: { 'a': { 3: (1.0, 0.0) }, 'b': { 3: (1.0, 0.0) } } } gamma_val = 1.0 mdp_ref_obj1 = MDPRefined(mdp_refined_data, gamma_val) mdp_rep_obj = mdp_ref_obj1.get_mdp_rep_for_rl_tabular() exploring_start_val = False softmax_flag = False episodes_limit = 10000 epsilon_val = 0.1 epsilon_half_life_val = 1000 learning_rate_val = 0.1 max_steps_val = 1000 state_ff = [lambda s: float(s)] sa_ff = [ lambda x: float(x[0]), lambda x: 1. if x[1] == 'a' else 0., lambda x: 1. if x[1] == 'b' else 0., lambda x: 1. if x[1] == 'c' else 0.,
def get_mdp_refined(self, model) -> MDPRefined: return MDPRefined(self.get_mdp_refined_dict(model), self.epoch_disc_factor)