def learn(self):
if self.resume:
self.load_agent_checkpoint()
else:
# delete tensorboard log file
log_tools.del_all_files_in_dir(self.result_dir)
explore_before_train(self.env, self.replay_buffer, self.explore_step)
print(
"==============================start train==================================="
)
obs = self.env.reset()
done = False
episode_reward = 0
episode_length = 0
while self.train_step < self.max_train_step:
action = self.choose_action(np.array(obs))
# print(action)
next_obs, reward, done, info = self.env.step(action)
episode_reward += reward
self.replay_buffer.add(obs, action, reward, next_obs, done)
obs = next_obs
episode_length += 1
actor_loss, critic_loss = self.train()
if done:
obs = self.env.reset()
done = False
self.episode_num += 1
print(
f"Total T: {self.train_step} Episode Num: {self.episode_num} "
f"Episode Length: {episode_length} Episode Reward: {episode_reward:.3f}"
)
self.tensorboard_writer.log_learn_data(
{
"episode_length": episode_length,
"episode_reward": episode_reward
}, self.train_step)
episode_reward = 0
episode_length = 0
if self.train_step % self.log_interval == 0:
self.store_agent_checkpoint()
self.tensorboard_writer.log_train_data(
{
"actor_loss": actor_loss,
"critic_loss": critic_loss
}, self.train_step)
if self.eval_freq > 0 and self.train_step % self.eval_freq == 0:
avg_reward, avg_length = evaluate(agent=self, episode_num=10)
self.tensorboard_writer.log_eval_data(
{
"eval_episode_length": avg_length,
"eval_episode_reward": avg_reward
}, self.train_step)
def learn(self):
"""Train TD3_BC without interacting with the environment (offline)"""
if self.resume:
self.load_agent_checkpoint()
else:
# delete tensorboard log file
log_tools.del_all_files_in_dir(self.result_dir)
while self.train_step < (int(self.max_train_step)):
actor_loss, critic_loss1, critic_loss2 = self.train()
if self.train_step % self.log_interval == 0:
self.store_agent_checkpoint()
self.tensorboard_writer.log_train_data(
{
"actor_loss": actor_loss,
"critic_loss1": critic_loss1,
"critic_loss2": critic_loss2
}, self.train_step)
if self.eval_freq > 0 and self.train_step % self.eval_freq == 0:
avg_reward, avg_length = evaluate(agent=self, episode_num=10)
self.tensorboard_writer.log_eval_data(
{
"eval_episode_length": avg_length,
"eval_episode_reward": avg_reward
}, self.train_step)
def learn(self):
if self.resume:
self.load_agent_checkpoint()
else:
# delete tensorboard log file
log_tools.del_all_files_in_dir(self.result_dir)
while self.train_step < (int(self.max_train_step)):
# train
q_loss1, q_loss2, actor_loss, alpha_prime_loss = self.train()
if self.train_step % self.eval_freq == 0:
avg_reward, avg_length = evaluate(agent=self, episode_num=10)
self.tensorboard_writer.log_eval_data(
{
"eval_episode_length": avg_length,
"eval_episode_reward": avg_reward
}, self.train_step)
if self.train_step % self.log_interval == 0:
self.store_agent_checkpoint()
self.tensorboard_writer.log_train_data(
{
"q_loss_1": q_loss1,
"q_loss_2": q_loss2,
"actor_loss": actor_loss,
"alpha_prime_loss": alpha_prime_loss
}, self.train_step)
def learn(self):
if self.resume:
self.load_agent_checkpoint()
else:
# delete tensorboard log file
log_tools.del_all_files_in_dir(self.result_dir)
explore_before_train(self.env, self.replay_buffer, self.explore_step)
print(
"==============================start train==================================="
)
obs = self.env.reset()
done = False
episode_reward = 0
episode_length = 0
while self.train_step < self.max_train_step:
action, _ = self.choose_action(np.array(obs))
next_obs, reward, done, info = self.env.step(action)
episode_reward += reward
self.replay_buffer.add(obs, action, reward, next_obs, done)
obs = next_obs
episode_length += 1
q_loss1, q_loss2, policy_loss, alpha_loss = self.train()
if done:
obs = self.env.reset()
done = False
self.episode_num += 1
print(
f"Total T: {self.train_step} Episode Num: {self.episode_num} "
f"Episode Length: {episode_length} Episode Reward: {episode_reward:.3f}"
)
self.tensorboard_writer.log_learn_data(
{
"episode_length": episode_length,
"episode_reward": episode_reward
}, self.train_step)
episode_reward = 0
episode_length = 0
if self.train_step % self.log_interval == 0:
self.store_agent_checkpoint()
self.tensorboard_writer.log_train_data(
{
"q_loss_1": q_loss1,
"q_loss_2": q_loss2,
"policy_loss": policy_loss,
"alpha_loss": alpha_loss
}, self.train_step)
def learn(self):
"""Train PLAS without interacting with the environment (offline)"""
if self.resume:
self.load_agent_checkpoint()
else:
# delete tensorboard log file
log_tools.del_all_files_in_dir(self.result_dir)
# Train CVAE before train agent
print(
"==============================Start to train CVAE=============================="
)
while self.cvae_iterations < (int(self.max_cvae_iterations)):
cvae_loss = self.train_cvae()
if self.cvae_iterations % 1000 == 0:
print("CVAE iteration:", self.cvae_iterations, "\t",
"CVAE Loss:", cvae_loss)
self.tensorboard_writer.log_train_data(
{"cvae_loss": cvae_loss}, self.cvae_iterations)
# Train Agent
print(
"==============================Start to train Agent=============================="
)
while self.train_step < (int(self.max_train_step)):
critic_loss, actor_loss = self.train()
if self.train_step % self.eval_freq == 0:
if self.train_step % self.eval_freq == 0:
avg_reward, avg_length = evaluate(agent=self,
episode_num=10)
self.tensorboard_writer.log_eval_data(
{
"eval_episode_length": avg_length,
"eval_episode_reward": avg_reward
}, self.train_step)
if self.train_step % self.log_interval == 0:
self.store_agent_checkpoint()
self.tensorboard_writer.log_train_data(
{
"critic_loss": critic_loss,
"actor_loss": actor_loss
}, self.train_step)
def learn(self):
if self.resume:
self.load_agent_checkpoint()
else:
# delete tensorboard log file
log_tools.del_all_files_in_dir(self.result_dir)
print(
"==============================start train==================================="
)
obs = self.env.reset()
done = False
episode_reward = 0
episode_length = 0
trajectory_length = 0
while self.time_step < self.max_time_step:
action, log_prob = self.choose_action(np.array(obs))
next_obs, reward, done, info = self.env.step(action)
value = self.critic_net(torch.tensor([obs],
dtype=torch.float32)).item()
episode_reward += reward
self.trajectory_buffer.add(obs, action, reward, done, log_prob,
value)
obs = next_obs
episode_length += 1
trajectory_length += 1
self.time_step += 1
if done:
obs = self.env.reset()
self.episode_num += 1
print(
f"Time Step: {self.time_step} Episode Num: {self.episode_num} "
f"Episode Length: {episode_length} Episode Reward: {episode_reward:.3f}"
)
self.tensorboard_writer.log_learn_data(
{
"episode_length": episode_length,
"episode_reward": episode_reward
}, self.time_step)
episode_reward = 0
episode_length = 0
if trajectory_length == self.trajectory_length:
last_val = self.critic_net(
torch.tensor([obs],
dtype=torch.float32)).item() if done else 0
self.trajectory_buffer.finish_path(
last_val=last_val,
gamma=self.gamma,
gae_lambda=self.gae_lambda,
gae_normalize=self.gae_normalize)
actor_loss, critic_loss = self.train()
trajectory_length = 0
if self.time_step % self.log_interval == 0:
self.store_agent_checkpoint()
self.tensorboard_writer.log_train_data(
{
"actor_loss": actor_loss,
"critic_loss": critic_loss
}, self.time_step)
if self.eval_freq > 0 and self.time_step % self.eval_freq == 0:
avg_reward, avg_length = evaluate(agent=self, episode_num=10)
self.tensorboard_writer.log_eval_data(
{
"eval_episode_length": avg_length,
"eval_episode_reward": avg_reward
}, self.time_step)
