面向分布式强化学习的经验回放框架（使用例子Demo）——Reverb: A Framework for Experience Replay

相关前文：

面向分布式强化学习的经验回放框架——Reverb: A Framework for Experience Replay

 

 

论文题目：

Reverb: A Framework for Experience Replay

地址：

https://arxiv.org/pdf/2102.04736.pdf

 

 

框架代码地址：

https://github.com/deepmind/reverb 

 

 

 

环境安装：

pip install dm-reverb[tensorflow]

 

 

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Example 1: Overlapping Trajectories

 

Inserting Overlapping Trajectories

import reverb
import tensorflow as tf


OBSERVATION_SPEC = tf.TensorSpec([10, 10], tf.uint8)
ACTION_SPEC = tf.TensorSpec([2], tf.float32)

def agent_step(unused_timestep) -> tf.Tensor:
  return tf.cast(tf.random.uniform(ACTION_SPEC.shape) > .5,
                 ACTION_SPEC.dtype)

def environment_step(unused_action) -> tf.Tensor:
  return tf.cast(tf.random.uniform(OBSERVATION_SPEC.shape, maxval=256),
                 OBSERVATION_SPEC.dtype)


# Initialize the reverb server.
simple_server = reverb.Server(
    tables=[
        reverb.Table(
            name='my_table',
            sampler=reverb.selectors.Prioritized(priority_exponent=0.8),
            remover=reverb.selectors.Fifo(),
            max_size=int(1e6),
            # Sets Rate Limiter to a low number for the examples.
            # Read the Rate Limiters section for usage info.
            rate_limiter=reverb.rate_limiters.MinSize(2),
            # The signature is optional but it is good practice to set it as it
            # enables data validation and easier dataset construction. Note that
            # we prefix all shapes with a 3 as the trajectories we'll be writing
            # consist of 3 timesteps.
            signature={
                'actions':
                    tf.TensorSpec([3, *ACTION_SPEC.shape], ACTION_SPEC.dtype),
                'observations':
                    tf.TensorSpec([3, *OBSERVATION_SPEC.shape],
                                  OBSERVATION_SPEC.dtype),
            },
        )
    ],
    # Sets the port to None to make the server pick one automatically.
    # This can be omitted as it's the default.
    port=9999)


# Initializes the reverb client on the same port as the server.
client = reverb.Client(f'localhost:{simple_server.port}')

# Dynamically adds trajectories of length 3 to 'my_table' using a client writer.

with client.trajectory_writer(num_keep_alive_refs=3) as writer:
  timestep = environment_step(None)
  for step in range(4):
    action = agent_step(timestep)
    writer.append({'action': action, 'observation': timestep})
    timestep = environment_step(action)

    if step >= 2:
      # In this example, the item consists of the 3 most recent timesteps that
      # were added to the writer and has a priority of 1.5.
      writer.create_item(
          table='my_table',
          priority=1.5,
          trajectory={
              'actions': writer.history['action'][-3:],
              'observations': writer.history['observation'][-3:],
          }
      )

server端和client端可以不在同一台主机上，这个例子是server和client在同一主机上。上面例子预设server端的端口为9999。其中server端主要功能为维持经验池中数据，client端可以sample，也可以insert，上面例子中client只进行了insert操作。

server端负责数据的sample和insert操作的定义，虽然客户端调用sample操作或insert操作，但是最后的具体执行还是在server端，毕竟数据是由server端所维护的。 

关于语句：

with client.trajectory_writer(num_keep_alive_refs=3) as writer:

个人的理解是，client中的数据如果需要进行insert操作，那么需要先申请一段缓存空间的，其中缓存空间的大小定义就是上面的参数num_keep_alive_refs，而writer.append操作是将数据写入到client端的缓存中，也就是num_keep_alive_refs所定义大小的缓存空间中，writer.create_item则是执行将加入到缓存空间中的数据insert到服务端的操作。这就需要保证writer.create_item的时候数据是需要保持在缓存中的，也就是说num_keep_alive_refs需要足够大，不然缓存空间中没有对应的数据而此时执行writer.create_item则是会报错的，当然我们也可以直接将num_keep_alive_refs设置为一个足够大的数，但是这样就会造成client端内存的浪费。

num_keep_alive_refs所定义大小的client端缓存空间中数据会由于writer.append操作造成旧数据移除,比如上面例子中如果设置语句：

with client.trajectory_writer(num_keep_alive_refs=2) as writer:

就会报错，但是设置语句：

with client.trajectory_writer(num_keep_alive_refs=4) as writer:

就不会报错。

 

 

Sampling Overlapping Trajectories in TensorFlow

在同一主机上执行server端代码，如下： 

import reverb
import tensorflow as tf


OBSERVATION_SPEC = tf.TensorSpec([10, 10], tf.uint8)
ACTION_SPEC = tf.TensorSpec([2], tf.float32)

def agent_step(unused_timestep) -> tf.Tensor:
  return tf.cast(tf.random.uniform(ACTION_SPEC.shape) > .5,
                 ACTION_SPEC.dtype)

def environment_step(unused_action) -> tf.Tensor:
  return tf.cast(tf.random.uniform(OBSERVATION_SPEC.shape, maxval=256),
                 OBSERVATION_SPEC.dtype)


# Initialize the reverb server.
simple_server = reverb.Server(
    tables=[
        reverb.Table(
            name='my_table',
            sampler=reverb.selectors.Prioritized(priority_exponent=0.8),
            remover=reverb.selectors.Fifo(),
            max_size=int(1e6),
            # Sets Rate Limiter to a low number for the examples.
            # Read the Rate Limiters section for usage info.
            rate_limiter=reverb.rate_limiters.MinSize(2),
            # The signature is optional but it is good practice to set it as it
            # enables data validation and easier dataset construction. Note that
            # we prefix all shapes with a 3 as the trajectories we'll be writing
            # consist of 3 timesteps.
            signature={
                'actions':
                    tf.TensorSpec([3, *ACTION_SPEC.shape], ACTION_SPEC.dtype),
                'observations':
                    tf.TensorSpec([3, *OBSERVATION_SPEC.shape],
                                  OBSERVATION_SPEC.dtype),
            },
        )
    ],
    # Sets the port to None to make the server pick one automatically.
    # This can be omitted as it's the default.
    port=9999)


# Initializes the reverb client on the same port as the server.
client = reverb.Client(f'localhost:{simple_server.port}')

# Dynamically adds trajectories of length 3 to 'my_table' using a client writer.

with client.trajectory_writer(num_keep_alive_refs=3) as writer:
  timestep = environment_step(None)
  for step in range(4):
    action = agent_step(timestep)
    writer.append({'action': action, 'observation': timestep})
    timestep = environment_step(action)

    if step >= 2:
      # In this example, the item consists of the 3 most recent timesteps that
      # were added to the writer and has a priority of 1.5.
      writer.create_item(
          table='my_table',
          priority=1.5,
          trajectory={
              'actions': writer.history['action'][-3:],
              'observations': writer.history['observation'][-3:],
          }
      )


import time
time.sleep(3333333)

并同时执行客户端代码：

import reverb


# Dataset samples sequences of length 3 and streams the timesteps one by one.
# This allows streaming large sequences that do not necessarily fit in memory.
dataset = reverb.TrajectoryDataset.from_table_signature(
  server_address=f'localhost:9999',
  table='my_table',
  max_in_flight_samples_per_worker=10)

# Batches 2 sequences together.
# Shapes of items is now [2, 3, 10, 10].
batched_dataset = dataset.batch(2)

for sample in batched_dataset.take(2):
  # Results in the following format.
  print(sample.info.key)              # ([2], uint64)
  print(sample.info.probability)      # ([2], float64)

  print(sample.data['observations'])  # ([2, 3, 10, 10], uint8)
  print(sample.data['actions'])       # ([2, 3, 2], float32)

其中，dataset.batch(2)语句定义每次sample时batch_size的大小，这条语句含义为定义大小。

语句：for sample in batched_dataset.take(2):是设置返回的迭代器可以迭代的此数，也就是说可以迭代返回的batch的个数，这里我们设置可以返回的batch个数为2，那么for循环就可以循环两次。

 

 

 

 

 

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其他相关代码见地址：

https://github.com/deepmind/reverb/blob/master/examples/demo.ipynb

https://github.com/deepmind/reverb/blob/master/examples/frame_stacking.ipynb

 

 

 

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