########################################################################################
radio_frame = 20
seeds = np.arange(100).tolist()
seeds = [98]
start_time = time.time()
for seed in seeds:
random.seed(seed)
np.random.seed(seed)
env = radio_environment(seed=seed)
agent = QLearner(seed=seed)
run_agent_fpa(env)
# run_agent_tabular(env)
# run_agent_deep(env)
# run_agent_optimal(env)
########################################################################################
end_time = time.time()
filename = 'figures/timing_M={}.txt'.format(env.M_ULA)
file = open(filename, 'w')
duration = 1000. * (end_time - start_time)
print('Execution time: {:4f} ms.\n'.format(duration))
ax.set_ylabel(r'$Q$')
ax_sec.set_ylabel(r'$L$')
plt.legend([plot1, plot2], [r'Average $Q$', r'Average loss'],
bbox_to_anchor=(0.1, 0.0, 0.80, 1),
bbox_transform=fig.transFigure,
loc='lower center',
ncol=3,
mode="expand",
borderaxespad=0.)
plt.tight_layout()
plt.savefig('output.pdf', format='pdf')
plt.show()
plt.close(fig)
seeds = np.arange(1).tolist()
for seed in seeds:
env = radio_environment(random_state=seed)
agent = QLearner(random_state=seed)
start_time = time.time()
run_agent_q(env)
end_time = time.time()
print('Simulation took {:.2f} minutes.'.format(
(end_time - start_time) / 60.))
########################################################################################