def demo_last(self, title='{} Agent - Fully Trained ', n_episodes=3, max_n_videos=3):
env = self.make_env_fn(**self.make_env_kargs, monitor_mode='evaluation', render=True, record=True)
title = title.format(self.__class__.__name__)
checkpoint_paths = self.get_cleaned_checkpoints()
last_ep = max(checkpoint_paths.keys())
self.policy_model.load_state_dict(torch.load(checkpoint_paths[last_ep]))
self.evaluate(self.policy_model, env, n_episodes=n_episodes)
env.close()
html_data = utils.get_gif_html(env_videos=env.videos,
title=title,
max_n_videos=max_n_videos)
del env
return html_data, title
def demo_progression(self, title='{} Agent - Progression', max_n_videos=5):
env = self.make_env_fn(**self.make_env_kargs, monitor_mode='evaluation', render=True, record=True)
title = title.format(self.__class__.__name__)
checkpoint_paths = self.get_cleaned_checkpoints()
for i in sorted(checkpoint_paths.keys()):
self.policy_model.load_state_dict(torch.load(checkpoint_paths[i]))
self.evaluate(self.policy_model, env, n_episodes=1)
env.close()
html_data = utils.get_gif_html(env_videos=env.videos,
title=title,
subtitle_eps=sorted(checkpoint_paths.keys()),
max_n_videos=max_n_videos)
del env
return html_data, title