def learn(self, writer, i_iter):
"""learn model"""
memory, log = self.collector.collect_samples(self.min_batch_size)
print(
f"Iter: {i_iter}, num steps: {log['num_steps']}, total reward: {log['total_reward']: .4f}, "
f"min reward: {log['min_episode_reward']: .4f}, max reward: {log['max_episode_reward']: .4f}, "
f"average reward: {log['avg_reward']: .4f}, sample time: {log['sample_time']: .4f}"
)
# record reward information
with writer.as_default():
tf.summary.scalar("total reward", log['total_reward'], i_iter)
tf.summary.scalar("average reward", log['avg_reward'], i_iter)
tf.summary.scalar("min reward", log['min_episode_reward'], i_iter)
tf.summary.scalar("max reward", log['max_episode_reward'], i_iter)
tf.summary.scalar("num steps", log['num_steps'], i_iter)
batch = memory.sample() # sample all items in memory
batch_state = NDOUBLE(batch.state)
batch_action = NDOUBLE(batch.action)
batch_reward = NDOUBLE(batch.reward)
batch_mask = NDOUBLE(batch.mask)
batch_value = self.value_net(batch_state)
batch_advantage, batch_return = estimate_advantages(
batch_reward, batch_mask, batch_value, self.gamma, self.tau)
v_loss, p_loss = vpg_step(self.policy_net, self.value_net,
self.optimizer_p, self.optimizer_v,
self.vpg_epochs, batch_state, batch_action,
batch_return, batch_advantage)
return v_loss, p_loss
def learn(self, writer, i_iter):
"""learn model"""
memory, log = self.collector.collect_samples(self.min_batch_size)
print(
f"Iter: {i_iter}, num steps: {log['num_steps']}, total reward: {log['total_reward']: .4f}, "
f"min reward: {log['min_episode_reward']: .4f}, max reward: {log['max_episode_reward']: .4f}, "
f"average reward: {log['avg_reward']: .4f}, sample time: {log['sample_time']: .4f}"
)
# record reward information
with writer.as_default():
tf.summary.scalar("total reward", log["total_reward"], i_iter)
tf.summary.scalar("average reward", log["avg_reward"], i_iter)
tf.summary.scalar("min reward", log["min_episode_reward"], i_iter)
tf.summary.scalar("max reward", log["max_episode_reward"], i_iter)
tf.summary.scalar("num steps", log["num_steps"], i_iter)
batch = memory.sample() # sample all items in memory
batch_state = NDOUBLE(batch.state)
batch_action = NDOUBLE(batch.action)
batch_reward = NDOUBLE(batch.reward)
batch_mask = NDOUBLE(batch.mask)
batch_log_prob = NDOUBLE(batch.log_prob)[:, None]
batch_value = tf.stop_gradient(self.value_net(batch_state))
batch_advantage, batch_return = estimate_advantages(
batch_reward, batch_mask, batch_value, self.gamma, self.tau
)
# update by TRPO
log_stats = trpo_step(
self.policy_net,
self.value_net,
self.optimizer_v,
batch_state,
batch_action,
batch_log_prob,
batch_advantage,
batch_return,
max_kl=self.max_kl,
cg_damping=self.damping,
vf_iters=10
)
with writer.as_default():
for k, v in log_stats.items():
tf.summary.scalar(k, v, i_iter)
writer.flush()
return log_stats
def learn(self, writer, i_iter):
"""learn model"""
memory, log = self.collector.collect_samples(self.min_batch_size)
print(
f"Iter: {i_iter}, num steps: {log['num_steps']}, total reward: {log['total_reward']: .4f}, "
f"min reward: {log['min_episode_reward']: .4f}, max reward: {log['max_episode_reward']: .4f}, "
f"average reward: {log['avg_reward']: .4f}, sample time: {log['sample_time']: .4f}"
)
# record reward information
with writer.as_default():
tf.summary.scalar("total reward", log['total_reward'], i_iter)
tf.summary.scalar("average reward", log['avg_reward'], i_iter)
tf.summary.scalar("min reward", log['min_episode_reward'], i_iter)
tf.summary.scalar("max reward", log['max_episode_reward'], i_iter)
tf.summary.scalar("num steps", log['num_steps'], i_iter)
batch = memory.sample() # sample all items in memory
batch_state = NDOUBLE(batch.state)
batch_action = NDOUBLE(batch.action)
batch_reward = NDOUBLE(batch.reward)
batch_mask = NDOUBLE(batch.mask)
batch_log_prob = NDOUBLE(batch.log_prob)[:, None]
batch_value = tf.stop_gradient(self.value_net(batch_state))
batch_advantage, batch_return = estimate_advantages(
batch_reward, batch_mask, batch_value, self.gamma, self.tau)
log_stats = {}
if self.ppo_mini_batch_size:
batch_size = batch_state.shape[0]
mini_batch_num = batch_size // self.ppo_mini_batch_size
for e in range(self.ppo_epochs):
perm = np.random.permutation(batch_size)
for i in range(mini_batch_num):
ind = perm[slice(
i * self.ppo_mini_batch_size,
min(batch_size, (i + 1) * self.ppo_mini_batch_size))]
log_stats = ppo_step(self.policy_net, self.value_net,
self.optimizer_p, self.optimizer_v, 1,
batch_state[ind], batch_action[ind],
batch_return[ind],
batch_advantage[ind],
batch_log_prob[ind],
self.clip_epsilon)
else:
for _ in range(self.ppo_epochs):
log_stats = ppo_step(self.policy_net, self.value_net,
self.optimizer_p, self.optimizer_v, 1,
batch_state, batch_action, batch_return,
batch_advantage, batch_log_prob,
self.clip_epsilon)
with writer.as_default():
tf.summary.histogram("ratio", log_stats["ratio"], i_iter)
tf.summary.scalar("policy loss", log_stats["policy_loss"], i_iter)
tf.summary.scalar("critic loss", log_stats["critic_loss"], i_iter)
writer.flush()
return log_stats