# comment this line if run on windows or OS X (default method)
mp.set_start_method('spawn')
print('Start exercise 2.5... ')
t1 = time.perf_counter()
with mp.Pool(mp.cpu_count()) as pool:
sample_av = np.array(pool.map(EpsGreedy(eps=0.1).rews_opts_nonstat, args))
const_step = np.array(pool.map(EpsGreedyConstant(eps=0.1, alpha=0.1).rews_opts_nonstat, args))
# got (2000, 2, 1000)-shaped arrays, axis=1 stands for rewards and optimals
t2 = time.perf_counter()
print(f'Done in {round(t2 - t1, 3)} sec')
# get average rewards
rewards = (sample_av[:, 0, :].mean(axis=0),
const_step[:, 0, :].mean(axis=0))
# get optimal action percentage
optimals = (Bandit.percent(sample_av[:, 1, :]),
Bandit.percent(const_step[:, 1, :]))
# plot
labels = ('Sample average\n' r'$\varepsilon=0.1$',
'Constant step-size\n' r'$\varepsilon=0.1, \alpha=0.1$')
Bandit.plot(rewards, labels, 'Average reward')
Bandit.plot(optimals, labels, '% Optimal action')
plt.show()
mp.set_start_method('spawn')
print('Stationary greedy started...')
t1 = time.perf_counter()
with mp.Pool(mp.cpu_count()) as pool:
def func(x):
return np.array(pool.map(EpsGreedy(eps=x).rews_opts_stat, args))
result = [func(eps) for eps in epsilons]
# get 3 (2000, 2, 1000)-shaped arrays, axis=1 stands for rewards and optimals
t2 = time.perf_counter()
print(f'Done in {round(t2 - t1, 3)} sec')
# get the average rewards
rewards = [pair[:, 0, :].mean(axis=0) for pair in result]
# get the percentage of the optimal actions
optimals = [Bandit.percent(pair[:, 1, :]) for pair in result]
# plotting
colors = ('green', 'blue', 'red')
labels = (r'$\varepsilon=0$ (greedy)', r'$\varepsilon=0.1$',
r'$\varepsilon=0.01$')
Bandit.plot(rewards, labels, 'Average reward')
Bandit.plot(optimals, labels, '% Optimal action')
plt.show()
bl01 = np.array(
pool.map(
GradientBaseline(true_value=4, alpha=0.1).optimals_stat, args))
bl04 = np.array(
pool.map(
GradientBaseline(true_value=4, alpha=0.4).optimals_stat, args))
no_bl01 = np.array(
pool.map(
GradientNoBaseline(true_value=4, alpha=0.1).optimals_stat,
args))
no_bl04 = np.array(
pool.map(
GradientNoBaseline(true_value=4, alpha=0.4).optimals_stat,
args))
t2 = time.perf_counter()
print(f'Done in {round(t2 - t1, 3)} sec')
result = [bl01, bl04, no_bl01, no_bl04]
# get percentages
result = [Bandit.percent(i) for i in result]
# plotting
labels = (r'with baseline, $\alpha=0.1$', r'with baseline, $\alpha=0.4$',
r'without baseline, $\alpha=0.1$',
r'without baseline, $\alpha=0.4$')
colors = ('blue', 'cornflowerblue', 'sienna', 'tan')
Bandit.plot(result, labels, '% Optimal action', colors=colors)
plt.show()
