def experiment1_test(
output_folder,
word_vectors,
agent,
episode_index,
testset_path='./dataset/conll2003/en/eng.testb',
):
# 初始化环境
env = Env(testset_path, word_vectors)
step = 0
s = env.reset()
print('[' + util.now_time() + "] start testing...")
while True:
# check task is ended
if env.end():
print('[' + util.now_time() + "] testing...done")
result_file = '%03d_episode_test.txt' % (episode_index + 1)
env.save_all_newlines_to_file(output_folder, result_file)
return evaluate.conlleval(output_folder, result_file)
# Choose Action a
a = agent.choose_action(s)
# Execute action
s_, r = env.step(a)
# Next status
step += 1
s = s_
def experiment1_train(
output_folder,
word_vectors,
n_episodes=300,
trainset_path='./dataset/conll2003/en/eng.train',
):
# 初始化环境
print('[' + util.now_time() + "] init environment...")
env = Env(trainset_path, word_vectors)
print('[' + util.now_time() + "] 环境初始化完毕")
# 初始化DQN
print('[' + util.now_time() + "] init agent...")
agent = DQN(n_actions=env.n_actions,
status_dim=env.status_dim,
action_dim=env.action_dim,
reward_dim=env.reward_dim)
print('[' + util.now_time() + "] agent初始化完毕")
# 迭代episodes
for i in range(n_episodes):
print('[' + util.now_time() + "] start episode %03d of learning..." %
(i + 1))
step = 0
s = env.reset()
while True:
# check task is ended
if env.end():
print('[' + util.now_time() +
"] episode %03d of learning...done" % (i + 1))
result_file = '%03d_episode_train.txt' % (i + 1)
env.save_all_newlines_to_file(output_folder, result_file)
train_eval = evaluate.conlleval(output_folder, result_file)
test_eval = experiment1_test(output_folder, word_vectors,
agent, i)
break
# Choose Action a
a = agent.choose_action(s)
# Execute action
# print('step %d' % step)
s_, r = env.step(a)
agent.store_transition(s, a, r, s_)
step += 1
s = s_
if step > 200 and step % 5 == 0:
agent.learn()
# plot and compare train and test set TODO
# plot(train_evals,test_evals)
agent.eval_network.save(output_folder + os.path.sep + 'ex1_eval_model',
overwrite=True)
