def dqn_batchnorm_prelu_no_rnd_init(state_shape,
action_dim,
show_plot_name=None,
hidden_size=32):
"""DDQNモデルを生成する.
Args:
state_shape: バッチを除いた入力値形状 tuple.
action_dim: アクション数、バッチを除いた出力値の要素数となる.
show_plot_name: 変換途中データのプロット表示するならプロット名を、それ以外は None.
hidden_size: 中間畳み込みレイヤのCH数.
Returns:
モデル.
"""
model = dnn.Root()
if not plot_enabled:
show_plot_name = None
if show_plot_name:
fig = plt.figure()
ax_cnv1 = fig.add_subplot(2, 3, 1)
ax_cnv1 = fig.add_subplot(2, 3, 1)
ax_cnv2 = fig.add_subplot(2, 3, 2)
ax_cnv3 = fig.add_subplot(2, 3, 3)
ax_dense_val = fig.add_subplot(2, 3, 4)
ax_dense_adv = fig.add_subplot(2, 3, 5)
ax_action = fig.add_subplot(2, 3, 6)
x_data = np.arange(action_dim)
with model as m:
c = m.nop()
c = c.conv2d(state_shape[0], hidden_size, 8,
4).batchnorm2d(hidden_size).prelu().plot_img(
show_plot_name, 0, ax_cnv1)
c = c.conv2d(hidden_size, hidden_size * 2, 4,
2).batchnorm2d(hidden_size * 2).prelu().plot_img(
show_plot_name, 0, ax_cnv2)
c = c.conv2d(hidden_size * 2, hidden_size * 2, 3,
1).batchnorm2d(hidden_size * 2).prelu().plot_img(
show_plot_name, 0, ax_cnv3)
f = c.flatten()
fc = f.calc_output_shape(state_shape)
val = f.dense(fc, 512).prelu().plot_dense(show_plot_name, 0,
ax_dense_val)
val = val.dense(512, 1)
adv = f.dense(fc, 512).prelu().plot_dense(show_plot_name, 0,
ax_dense_adv)
adv = adv.dense(512, action_dim)
ave = adv.mean(1, keepdims=True)
m.gate('merge', (lambda o, val, adv, ave: val + adv - ave), val, adv,
ave).plot_action(show_plot_name, 0, ax_action, x_data)
model.build()
return model
def dqn_prelu_long_dense(state_shape,
action_dim,
show_plot_name=None,
hidden_size=32):
"""DDQNモデルを生成する.
Args:
state_shape: バッチを除いた入力値形状 tuple.
action_dim: アクション数、バッチを除いた出力値の要素数となる.
show_plot_name: 変換途中データのプロット表示するならプロット名を、それ以外は None.
hidden_size: 中間畳み込みレイヤのCH数.
Returns:
モデル.
"""
model = dnn.Root()
if not plot_enabled:
show_plot_name = None
if show_plot_name:
fig = plt.figure()
fig.suptitle(show_plot_name, fontsize=12)
ax_cnv1 = fig.add_subplot(2, 3, 1)
ax_cnv1 = fig.add_subplot(2, 3, 1)
ax_cnv2 = fig.add_subplot(2, 3, 2)
ax_cnv3 = fig.add_subplot(2, 3, 3)
ax_dense_val = fig.add_subplot(2, 3, 4)
ax_dense_adv = fig.add_subplot(2, 3, 5)
ax_action = fig.add_subplot(2, 3, 6)
x_data = np.arange(action_dim)
else:
ax_cnv1 = None
ax_cnv1 = None
ax_cnv2 = None
ax_cnv3 = None
ax_dense_val = None
ax_dense_adv = None
ax_action = None
x_data = None
with model as m:
c = m.conv2d(state_shape[0], hidden_size, 8,
4).prelu().plot_img('conv 1', 0, ax_cnv1)
c = c.conv2d(hidden_size, hidden_size * 2, 4,
2).prelu().plot_img('conv 2', 0, ax_cnv2)
c = c.conv2d(hidden_size * 2, hidden_size * 2, 3,
1).prelu().plot_img('conv 3', 0, ax_cnv3)
f = c.flatten()
fc = f.calc_output_shape(state_shape)
val = f.dense(fc, 512).prelu().dense(512, 512).prelu().plot_dense(
'dense val', 0, ax_dense_val)
val = val.dense(512, 1)
adv = f.dense(fc, 512).prelu().dense(512, 512).prelu().plot_dense(
'dense adv', 0, ax_dense_adv)
adv = adv.dense(512, action_dim)
ave = adv.mean(1, keepdims=True)
m.gate('merge', (lambda o, val, adv, ave: val + adv - ave), val, adv,
ave).plot_action('action', 0, ax_action, x_data)
for w in model.get_weights():
nn.init.normal_(w, 0, 0.02)
model.build()
return model