tensorflow - 为什么 tf_agent 变量即使在训练后也不会改变？

Question

tf 版本:2.3.0

import numpy as np
import tensorflow as tf

from tf_agents.agents.reinforce import reinforce_agent
from tf_agents.drivers import dynamic_step_driver
from tf_agents.environments import suite_gym, tf_py_environment
from tf_agents.eval import metric_utils
from tf_agents.metrics import tf_metrics
from tf_agents.networks import actor_distribution_network
from tf_agents.replay_buffers import tf_uniform_replay_buffer
from tf_agents.trajectories import trajectory
from tf_agents.utils import common

tf.compat.v1.enable_v2_behavior()

env_name='CartPole-v0'
num_iterations=1
collect_episodes_per_iteration=2
replay_buffer_capacity=2000

fc_layer_params=(100, )

learning_rate=1e-3
log_interval=5
num_eval_episodes=10
eval_interval=10

env=suite_gym.load(env_name)

env.reset()

time_step=env.reset()

train_py_env=suite_gym.load(env_name)

train_env=tf_py_environment.TFPyEnvironment(train_py_env)
actor_net=actor_distribution_network.ActorDistributionNetwork(train_env.observation_spec(), train_env.action_spec(), fc_layer_params=fc_layer_params)

optimizer=tf.compat.v1.train.AdamOptimizer(learning_rate=learning_rate)
train_step_counter=tf.compat.v2.Variable(0)

tf_agent=reinforce_agent.ReinforceAgent(train_env.time_step_spec(),
train_env.action_spec(),
actor_network=actor_net,
optimizer=optimizer,
normalize_returns=True,
train_step_counter=train_step_counter)

tf_agent.initialize()

eval_policy=tf_agent.policy
collect_policy=tf_agent.collect_policy

replay_buffer=tf_uniform_replay_buffer.TFUniformReplayBuffer(
data_spec=tf_agent.collect_data_spec,
batch_size=train_env.batch_size,
max_length=replay_buffer_capacity
)

tf_agent.train=common.function(tf_agent.train)

def collect_episode(environment, policy, num_episodes):
    episode_counter=0
    environment.reset()

    while episode_counter<num_episodes:
        time_step=environment.current_time_step()
        action_step=policy.action(time_step)
        next_time_step=environment.step(action_step.action)
        traj=trajectory.from_transition(time_step, action_step, next_time_step)
      
        replay_buffer.add_batch(traj)

        if traj.is_boundary():
            episode_counter+=1

collect_episode(train_env, tf_agent.collect_policy, 1)
experience=replay_buffer.gather_all()

for _ in range(num_iterations):
    collect_episode(train_env, tf_agent.collect_policy, collect_episodes_per_iteration)

    from copy import copy

    before=copy(tf_agent.trainable_variables)
    experience=replay_buffer.gather_all()
    train_loss=tf_agent.train(experience)
    replay_buffer.clear()

    after=copy(tf_agent.trainable_variables)
    print('before==after?', before==after)
   

---

https://www.tensorflow.org/agents/tutorials/6_reinforce_tutorial

我一直在关注 TFAgents 的教程，但我发现

before=copy(tf_agent.trainable_variables)

tf_agent.train(experience)     

after=copy(tf_agent.trainable_variables)

然后“之前”应该与“之后”不同。 但是 (before==after) 总是代表 'True'。

我对此很困惑。我认为梯度可能为零。

然而，这是不合理的，导致模型的损失在训练步骤中持续减少。

在reinforce_agent模块上，gradient tape step写的很好..

我找不到问题所在... 无论训练步骤如何，甚至 tf_agent.policy.trainable_variables 都是相同的。

Answer 1

原因是 tf_agent.trainable_variables 是 tf.Variables(张量)的元组。使用 copy 复制这些张量并没有达到您的预期，发生的是 before 中的张量随着代理训练而更新。要真正看到差异，请尝试以下代码:

before = []

for element in tf_agent.trainable_variables:
  before.append(tf.identity(element))

tf_agent.train(experience)

after = []

for element in tf_agent.trainable_variables:
  after.append(tf.identity(element))
print(before == after)