def train(model, maze, epsilon=0.1):
if model_file:
print("loading weights from file: %s" % (model_file, ))
model.load_weights(model_file)
my_maze = Maze(maze)
# 存储episode
git_store = Git(model, max_save_size=max_save_size)
win_history = []
hsize = my_maze.maze.size // 2
win_rate = 0.0
for epoch in range(epoch_num):
loss = 0.0
rat_cell = random.choice(my_maze.free_cells)
my_maze.reset(rat_cell)
game_over = False
state = my_maze.get_current_state()
n_episodes = 0
while not game_over:
valid_actions = my_maze.valid_actions()
if not valid_actions: break
state_now = state
# epsilon-贪心算法
if np.random.rand() < epsilon:
action = random.choice(valid_actions)
else:
action = np.argmax(git_store.predict(state_now))
# 实施action
state_next, reward, game_status = my_maze.act(
action, my_maze.get_current_state)
if game_status == 'win':
win_history.append(1)
game_over = True
elif game_status == 'lose':
win_history.append(0)
game_over = True
else:
game_over = False
#保存episode
episode = [state_now, action, reward, state_next, game_over]
git_store.remember(episode)
n_episodes += 1
# 训练网络
inputs, targets = git_store.get_data(batch_size=data_size)
h = model.fit(
inputs,
targets,
epochs=8,
batch_size=16,
verbose=0,
)
loss = model.evaluate(inputs, targets, verbose=0)
if len(win_history) > hsize:
win_rate = sum(win_history[-hsize:]) / hsize
template = "Epoch: {:03d}/{:d} Loss: {:.4f} Episodes: {:d} Win count: {:d} Win rate: {:.3f}"
print(
template.format(epoch, epoch_num - 1, loss, n_episodes,
sum(win_history), win_rate))
# Save trained model weights and architecture, this will be used by the visualization code
h5file = save_file + ".h5"
json_file = save_file + ".json"
model.save_weights(h5file, overwrite=True)
with open(json_file, "w") as outfile:
json.dump(model.to_json(), outfile)
# we simply check if training has exhausted all free cells and if in all
# cases the agent won
if win_rate > 0.9: epsilon = 0.05
'''if sum(win_history[-hsize:]) == hsize and completion_check(model, my_maze):
print("Reached 100%% win rate at epoch: %d" % (epoch,))
break'''
print('files: %s, %s' % (h5file, json_file))
print("n_epoch: %d, max_mem: %d, data: %d, time: %s" %
(epoch, max_save_size, data_size))
