def run_dp_train(self):
sa_costs = None
with open('../Pendulum/dp_sa_cost.p', 'rb') as f:
sa_costs = pickle.load(f).flatten()
sa_samples = []
a_grid = np.linspace(-2, 2, 9)
s1_grid = np.linspace(0, 2*np.pi, 51)
s2_grid = np.linspace(-10, 10, 51)
for i, c in enumerate(sa_costs):
a = i / (51 * 51)
ss = i % (51 * 51)
s2, s1 = ss / 51, ss % 51
a = a_grid[a]
s1 = s1_grid[s1]
s2 = s2_grid[s2]
sa_samples.append([s1, s2, a, c])
sa_samples = np.array(sa_samples)
n = len(sa_samples)
for i in range(0):
sample_size = n if i%100==0 else 50
inds = np.random.choice(range(n), sample_size, False)
#t = np.array(random.sample(sa_samples, sample_size))
t = sa_samples[inds]
if i%100 == 0:
print 'iter', i
mse = self.current_net.mse_q(t[:,0:3], -t[:,3][:,np.newaxis])
learn_rate = self.current_net.get_learn_rate()
print 'mse', mse, 'learn_rate', learn_rate
self.mse_hist.append(mse)
self.learn_rate_hist.append(learn_rate)
else:
self.current_net.train(t[:,0:3], -t[:,3][:,np.newaxis])
#plt.plot(mses); plt.show()
vis = NetVisualizer(self.current_net)
vis.q_heat_map()
