def check_maximum_point():
# step5_priorにおいて，最も良い値を出力する条件を調査する為の関数
resolution = 100
node_pos, input_nodes, input_vectors,\
output_nodes, output_vectors, frozen_nodes,\
edges_indices, edges_thickness, frozen_nodes = easy_dev()
env = BarFemGym(node_pos, input_nodes, input_vectors,
output_nodes, output_vectors, frozen_nodes,
edges_indices, edges_thickness, frozen_nodes)
env.reset()
reward = env.calculate_simulation()
rewards = np.zeros((resolution, resolution))
max = 0
for ix, x in enumerate(tqdm(np.linspace(0, 1, resolution))):
for iy, y in enumerate(np.linspace(1, 0, resolution)):
env.reset()
action = {}
action['which_node'] = np.array([3, 4])
action['end'] = 0
action['edge_thickness'] = np.array([1])
action['new_node'] = np.array([[x, y]])
next_nodes_pos, _, done, _ = env.step(action)
env.input_nodes = [2, 4]
env.input_vectors = np.array([[1, 0], [0, 1]])
reward = env.calculate_simulation()
if max < reward:
max = reward
max_x = x
max_y = y
max_reward = reward
rewards[iy, ix] = reward
fig = plt.figure(figsize=(6, 6))
ax = fig.add_subplot(111)
im = plt.imshow(rewards, extent=(0, 1, 0, 1))
plt.colorbar(im)
ax.set_xlabel(r"x", fontsize=20)
ax.set_ylabel(r"y", fontsize=20)
ax.tick_params(axis='x', labelsize=20)
ax.tick_params(axis='y', labelsize=20)
plt.savefig("distribution.png")
print(max_x, max_y)
print(max_reward)
def actor_gcn_critic_gcn(max_episodes=5000,
test_name="test",
log_file=False,
save_pth=False):
"""Actor-Criticを行う．Actor,CriticはGCN
Actorの指定できるものは，一つのエッジのみの幅を選択できる．
max_episodes:学習回数
test_name:保存ファイルの名前
log_file: Trueにすると，progress.txtに損失関数などの情報のログをとる．"""
history = {}
history['epoch'] = []
history['result_efficiency'] = []
history['x'] = []
history['x_mean'] = []
history['x_sigma'] = []
history['y'] = []
history['y_mean'] = []
history['y_sigma'] = []
history['advantage'] = []
history['critic_value'] = []
log_dir = "confirm/step5_entropy/a_gcn_c_gcn_results/{}".format(test_name)
assert not os.path.exists(log_dir), "already folder exists"
if log_file:
log_file = log_dir
else:
log_file = None
os.makedirs(log_dir, exist_ok=True)
node_pos, input_nodes, input_vectors,\
output_nodes, output_vectors, frozen_nodes,\
edges_indices, edges_thickness, frozen_nodes = easy_dev()
env = BarFemGym(node_pos, input_nodes, input_vectors, output_nodes,
output_vectors, frozen_nodes, edges_indices,
edges_thickness, frozen_nodes)
env.reset()
lr_actor = 1e-4
lr_critic = 1e-3
weight_decay = 1e-2
gamma = 0.99
device = torch.device('cpu')
criticNet = CriticNetwork_GCN(2, 1, 400, 400).to(device).double()
x_y_Net = X_Y_Actor(2, 1, 400, 400).to(device).double()
node1Net = Select_node1_model(2, 1, 400, 400).to(device).double()
node2Net = Select_node2_model(400 + 2, 400).to(
device).double() # 400+2における400は，Select_node1_modelのinput3の部分に対応
optimizer_node1 = optim.Adam(node1Net.parameters(), lr=lr_actor)
optimizer_node2 = optim.Adam(node2Net.parameters(), lr=lr_actor)
optimizer_xy = optim.Adam(x_y_Net.parameters(), lr=lr_actor)
optimizer_critic = optim.Adam(criticNet.parameters(),
lr=lr_critic,
weight_decay=weight_decay)
for episode in tqdm(range(max_episodes)):
if log_file:
with open(os.path.join(log_dir, "progress.txt"), mode='a') as f:
print('\nepoch:', episode, file=f)
env = BarFemGym(node_pos, input_nodes, input_vectors, output_nodes,
output_vectors, frozen_nodes, edges_indices,
edges_thickness, frozen_nodes)
env.reset()
nodes_pos, edges_indices, edges_thickness, node_adj = env.extract_node_edge_info(
)
action = select_action_gcn_critic_gcn(env,
criticNet,
node1Net,
node2Net,
x_y_Net,
device,
log_dir=log_file,
history=history)
next_nodes_pos, _, done, _ = env.step(action)
if 4 in action['which_node']:
env.input_nodes = [2, 4]
env.input_vectors = np.array([[1, 0], [0, 1]])
if 2 in action['which_node'] and 4 in action[
'which_node']: # TODO [2,4]を選択しないように学習させる
reward = np.array([0])
else:
reward = env.calculate_simulation()
criticNet.rewards.append(reward)
loss = finish_episode(criticNet,
x_y_Net,
node1Net,
node2Net,
optimizer_critic,
optimizer_xy,
optimizer_node1,
optimizer_node2,
gamma,
log_dir=log_file,
history=history)
history['epoch'].append(episode + 1)
history['result_efficiency'].append(reward)
plot_efficiency_history(
history, os.path.join(log_dir, 'learning_effi_curve.png'))
if episode % 100 == 0:
if save_pth:
save_model(criticNet,
x_y_Net,
os.path.join(log_dir, "pth"),
save_name=str(episode))
env.close()
with open(os.path.join(log_dir, 'history.pkl'), 'wb') as f:
pickle.dump(history, f)
return history
