def save(self):
# list and ',' = list[0]
line, = self.ax.plot(self.X[0::3], self.Y[0::3], 'b')
if self.ax.get_title() != '':
name = self.ax.get_title().replace(' ', '_')
self.fig.savefig(self.save_path + name + '.png')
else:
name = time_seq()
self.fig.savefig(self.save_path + name + '.png')
def __init__(self, agent, env, config: Config, record=False):
self.agent = agent
self.config = config
#self.outputdir = get_output_folder(self.config.output, self.config.env)
if record:
os.makedirs('video', exist_ok=True)
filepath = self.outputdir + '/video/' + config.env + '-' + time_seq()
env = wrappers.Monitor(env, filepath,
video_callable=lambda episode_id: episode_id % self.config.record_ep_interval == 0)
self.env = env
self.env.seed(config.seed)
self.agent.is_training = True
record=args.record)
trainer.train()
elif args.retrain:
if args.retrain_model is None:
print('please add the retrain model path:', '--retrain_model xxxx')
exit(0)
ep, step = agent.load_checkpoint(args.retrain_model)
trainer = Trainer(agent, env, config,
record=args.record)
trainer.train(ep, step)
elif args.test:
if args.model_path is None:
print('please add the model path:', '--model_path xxxx')
exit(0)
# record
if args.record:
os.makedirs('video', exist_ok=True)
filepath = 'video/' + args.env + '-' + time_seq()
env = wrappers.Monitor(env, filepath, video_callable=lambda episode_id: episode_id % 25 == 0)
tester = Tester(agent, env,
model_path=args.model_path)
tester.test()
else:
print('choose train or test:', '--train or --test')