step_stdev,
debug=False,
mcmc_norm=mcmc_norm)
map_w, map_u_sa, w_chain, u_chain = birl.sample_posterior(
demonstrations, num_samples, False)
print("Birl complete")
if debug:
print("-------")
print("true weights", true_w)
print("features")
utils.display_onehot_state_features(mdp_env)
print("optimal policy")
utils.print_policy_from_occupancies(opt_u_sa, mdp_env)
print("optimal values")
v = mdp.get_state_values(opt_u_sa, mdp_env)
utils.print_as_grid(v, mdp_env)
if debug:
print("map_weights", map_w)
map_r = np.dot(mdp_env.state_features, map_w)
print("MAP reward")
utils.print_as_grid(map_r, mdp_env)
print("Map policy")
utils.print_policy_from_occupancies(map_u_sa, mdp_env)
w_chain_burned = w_chain[burn::skip]
###compute mean reward policy
mean_w = np.mean(w_chain_burned, axis=0)
weights = utils.sample_l2_ball(num_features)
print("weights", weights)
gamma = 0.99
#let's look at all starting states for now
init_dist = np.ones(num_states) / num_states
# init_states = [10]
# for si in init_states:
# init_dist[si] = 1.0 / len(init_states)
#no terminal
term_states = []
mdp_env = mdp.FeaturizedGridMDP(num_rows, num_cols, state_features,
weights, gamma, init_dist, term_states)
return mdp_env
if __name__ == "__main__":
#mdp_env = machine_teaching_toy_featurized()
# mdp_env = lava_ambiguous_aaai18()
mdp_env = random_gridworld_corner_terminal(6, 6, 5)
print("features")
utils.display_onehot_state_features(mdp_env)
u_sa = mdp.solve_mdp_lp(mdp_env, debug=True)
print("optimal policy")
utils.print_policy_from_occupancies(u_sa, mdp_env)
print("optimal values")
v = mdp.get_state_values(u_sa, mdp_env)
utils.print_as_grid(v, mdp_env)
init_distribution = np.ones(num_states)/num_states #uniform distribution
mdp_env = mdp.MachineReplacementMDP(num_states, r_sa, gamma, init_distribution)
#print(mdp_env.Ps)
print("mean MDP reward", r_sa)
u_sa = mdp.solve_mdp_lp(mdp_env, debug=True)
print("mean policy from posterior")
utils.print_stochastic_policy_action_probs(u_sa, mdp_env)
print("MAP/Mean policy from posterior")
utils.print_policy_from_occupancies(u_sa, mdp_env)
print("rewards")
print(mdp_env.r_sa)
print("expected value = ", np.dot(u_sa, r_sa))
stoch_pi = utils.get_optimal_policy_from_usa(u_sa, mdp_env)
print("expected return", mdp.get_policy_expected_return(stoch_pi, mdp_env))
print("values", mdp.get_state_values(u_sa, mdp_env))
print('q-values', mdp.get_q_values(u_sa, mdp_env))
#print(posterior)
#print(posterior.shape)
#run CVaR optimization, maybe just the robust version for now
u_expert = np.zeros(mdp_env.num_actions * mdp_env.num_states)
# print("solving for CVaR optimal policy")
posterior_probs = np.ones(num_samples) / num_samples #uniform dist since samples from MCMC
# cvar_opt_usa, cvar, exp_ret = mdp.solve_max_cvar_policy(mdp_env, u_expert, posterior, posterior_probs, alpha, False, lamda)
