def run_sarsa(start, goal, Xrange, Vrange, plot_data_pid):
sarsa_plot_data = list()
sarsa_plot_data.append(plot_data_pid)
for i in range(1, 9):
sarsa = Sarsa(start, goal, Xrange, Vrange, n=i)
sarsa.train(epoch=EPOCH, max_episode_length=MAX_EPISODE_LENGTH)
sarsa_plot_data.append(sarsa.episodes)
plot_with_n(sarsa_plot_data)
# avg = np.average(np.array(rewards), axis=0)
# std = np.std(np.array(rewards), axis=0)
# maximumEpisodes = avg.shape[0]
# plt.errorbar(np.array([i for i in range(maximumEpisodes)]), avg, std, marker='^', ecolor='g')
# plt.show()
type = "linear"
# best parameter, order 3, e 0.2, alpha 0.5
# best parameter, order 5, e 0.2, alpha 0.5
for e in [0.3]:#, 0.1, 0.01, 0.3, 0.4]:
for order in [3]: #, 5]:
for alpha in [0.01]:#, 0.0001, 0.0005, 0.0009, 0.001, 0.005, 0.009, 0.01, 0.05, 0.09, 0.1, 0.5, 0.9]:
rewards = []
print("Alpha: ", alpha)
for t in tqdm(range(trails)):
# print("Alpha: %s, Trail: %s" %(alpha, t))
td = Sarsa(gamma, alpha, env, state_space, steps, e, plot=plot, order=order, discount=discount)
td.train(episodes)
rewards.append(td.reward)
avg = np.average(np.array(rewards), axis=0)
std = np.std(np.array(rewards), axis=0)
maximumEpisodes = avg.shape[0]
plt.errorbar(np.array([i for i in range(maximumEpisodes)]), avg, std, marker='^', ecolor='g')
#name = "Sarsa/figures/%s/cartPole_type_%s_order%s_alpha%s_e%s.jpg" %(type, type, order, alpha, e)
name = "Grid_alpha%s_e%s.jpg" % (alpha, e)
pickle.dump(avg, open(name, "wb"))
plt.xlabel("Number of episodes")
plt.ylabel("Total Reward")
# plt.savefig(name)
# plt.close()
