val = model.env.env_method("_send_config")[0]
names, values= val[0],val[1]
for i in range(len(values)):
sql = ''' INSERT INTO parameters(simu, type, step, value)
VALUES(?,?,?,?) '''
val = (model_name, names[i], 0, float(values[i]))
cur.execute(sql,val)
conn.commit()
cur.close()
conn.close()
for i in range(args.total_steps//args.save_every):
model.learn(total_timesteps=args.save_every, tb_log_name=model_name, reset_num_timesteps=False, callback=callback)
if normalize:
env.save_running_average(workDirectory+"/resultats/"+model_name+"/normalizeData")
model.save(workDirectory+"/resultats/"+model_name+"/"+model_name)
os.system("python3 makegif.py --algo "+args.algo+" --dir ./server/assets/"+model_name+"_"+str((i+1)*args.save_every)+"_steps.gif --name "+model_name)
print("\n saved at "+str((i+1)*args.save_every))
model.save(workDirectory+"/resultats/"+model_name+"/"+model_name)
if normalize:
env.save_running_average(workDirectory+"/resultats/"+model_name+"/normalizeData")
env = DummyVecEnv([lambda: e.AidaBulletEnv(commands,
render = False,
on_rack = False,
default_reward = args.default_reward,
height_weight = args.height_weight,
orientation_weight = args.orientation_weight,
direction_weight = args.direction_weight,
speed_weight = args.speed_weight,
model.learn(n_timesteps, **kwargs)
if args.teleop:
env.wait()
env.exit()
time.sleep(0.5)
else:
# Close the connection properly
env.reset()
if isinstance(env, VecFrameStack):
env = env.venv
# HACK to bypass Monitor wrapper
env.envs[0].env.exit_scene()
# Save trained model
model.save(os.path.join(save_path, ENV_ID), cloudpickle=True)
# Save hyperparams
with open(os.path.join(params_path, 'config.yml'), 'w') as f:
yaml.dump(saved_hyperparams, f)
if args.save_vae and vae is not None:
print("Saving VAE")
vae.save(os.path.join(params_path, 'vae'))
if normalize:
# Unwrap
if isinstance(env, VecFrameStack):
env = env.venv
# Important: save the running average, for testing the agent we need that normalization
env.save_running_average(params_path)
def callback(_locals, _globals):
global n_steps
n_steps += 1
if n_steps % 50000 == 0 or n_steps == 10000:
print('Saving: ', n_steps)
save_path = 'checkpoints/yumi/her/her_{}_task_{}_{}.npy'.format(
name, args.task, n_steps)
os.makedirs(os.path.dirname(save_path), exist_ok=True)
model.save(save_path)
return True
model = HER('MlpPolicy',
env,
model_class=DDPG,
verbose=1,
tensorboard_log=log_dir,
**dict(random_exploration=.2))
model.learn(total_timesteps=total_timesteps, callback=callback)
model.save("her-yumi-{}-final".format(n_steps))
env.save_running_average(log_dir)
obs = env.reset()
for i in range(100):
action, _states = model.predict(obs)
obs, rewards, dones, info = env.step(action)
env.render()
