(1, 0.1),
],
no_action_probability=0.0)
mdp_solvers = {
'Value Iteration': gw.run_value_iterations,
'Policy Iteration': gw.run_policy_iterations
}
for solver_name, solver_fn in mdp_solvers.items():
print('Final result of {}:'.format(solver_name))
policy_grids, utility_grids = solver_fn(iterations=25, discount=0.5)
print(policy_grids[:, :, -1])
print(utility_grids[:, :, -1])
plt.figure()
gw.plot_policy(utility_grids[:, :, -1])
plot_convergence(utility_grids, policy_grids)
plt.show()
ql = QLearner(num_states=(shape[0] * shape[1]),
num_actions=4,
learning_rate=0.8,
discount_rate=0.9,
random_action_prob=0.5,
random_action_decay_rate=0.99,
dyna_iterations=0)
start_state = gw.grid_coordinates_to_indices(start)
iterations = 1000
flat_policies, flat_utilities = ql.learn(start_state,
