model.train_on_batch(x=np.expand_dims(last_state, axis=0),
y=identity[action:action + 1])
print(
f" training happens: {reward}\nx_pos:{info['x_pos']}"
)
elif reward < 0:
action = env.action_space.sample()
model.train_on_batch(x=np.expand_dims(last_state, axis=0),
y=identity[action:action + 1])
print(
f" training happens: {reward}\nx_pos:{info['x_pos']}"
)
env.render()
x_pos = info["x_pos"]
if x_pos > max_x_pos:
max_x_pos = x_pos
io.write_settings("max_x_pos", int(max_x_pos))
if info["life"] == 2:
failer_mode = False
perfect_model = model
model.save(model_file_path)
if info["stage"] == 2:
io.write_settings("max_x_pos", int(max_x_pos))
model.save(final_model_file_path)
input("congraduations!")
exit()
env.close()
last_info = info
action = 3
state, reward, done, info = env.step(action)
history_actions.append(action)
history_actions = history_actions[-200:]
history_x_pos.append(info['x_pos'])
history_x_pos = history_x_pos[-200:]
history_y_pos.append(info['y_pos'])
history_y_pos = history_y_pos[-200:]
if isinstance(last_state, (np.ndarray, np.generic)):
if len(history_actions) >= 101 and len(history_x_pos) >= 101 and len(history_y_pos) >= 101:
training_couting += 1
io.write_settings("training_couting", int(training_couting))
print(f" learning happend with action: {SIMPLE_MOVEMENT[action]}")
train_once(last_state, history_actions[-101:-1], history_x_pos[-101:-1], history_y_pos[-101:-1], action, reward)
temp = 15
while 1:
if isinstance(state, (np.ndarray, np.generic)) and info != None:
last_state = state
last_info = info
action = 2
state, reward, done, info = env.step(action)
history_actions.append(action)
history_actions = history_actions[-200:]
