EnvironmentLogger

EnvLogger is a standard dm_env.Environment class wrapper that records interactions between a real environment and an agent. These interactions are saved on disk as trajectories and can be retrieved in whole, by individual timesteps or by specific episodes.

Metadata

To better categorize your logged data, you may want to add some tags in the metadata when you construct the logger wrapper. The metadata is written once per EnvLogger instance.

env = envlogger.EnvLogger(
    env,
    data_directory='/tmp/experiment_logs',
    metadata={
        'environment_type': 'dm_control',
        'agent_type': 'D4PG'
    })

How to use Envlogger

Most of the time, it is just a one-liner wrapper, e.g.

import envlogger
from envlogger.testing import catch_env
import numpy as np

env = catch_env.Catch()
with envlogger.EnvLogger(
    env, data_directory='/tmp/experiment_logs') as env:

  env.reset()
  for step in range(100):
    action = np.random.randint(low=0, high=3)
    timestep = env.step(action)

Full example of random agent in Catch is available here: random_agent_catch.py

Step metadata

Envlogger also allows to record custom metadata per step by defining a function that can be passed to the wrapper. In this example, we want to record the timestamp of when each step was produced:

  def step_fn(unused_timestep, unused_action, unused_env):
    return {'timestamp': time.time()}

  ...

  with envlogger.Envlogger(
    env,
    data_directory='/tmp/experiment_logs',
    step_fn=step_fn) as env:

  ...

Episode metadata

Recording custom episode metadata is also possible by providing a callback. This callback is invoked at every step but only the last value returned that is not None (if any) is stored.

In the following example, we only store the timestamp of the first step of the episode.

  def episode_fn(timestep, unused_action, unused_env):
    if timestemp.first:
      return {'timestamp': time.time()}
    else:
      return None

  ...

  with envlogger.Envlogger(
    env,
    data_directory=FLAGS.trajectories_dir,
    episode_fn=episode_fn) as env:

  ...

TFDS backend

Envlogger supports writing data that is directly compatible with TFDS and RLDS.

For that, you need to indicate the specs of your environment in terms of TFDS Features using an RLDS DatasetConfig like in the example (note that the config can include step_metadata_infoand episode metadata_info).

  dataset_config = tfds.rlds.rlds_base.DatasetConfig(
      name='catch_example',
      observation_info=tfds.features.Tensor(
          shape=(10, 5), dtype=tf.float32,
          encoding=tfds.features.Encoding.ZLIB),
      action_info=tf.int64,
      reward_info=tf.float64,
      discount_info=tf.float64)

And then use the TFDS Backend when instantiating the Envlogger:

envlogger.EnvLogger(
      env,
      backend = tfds_backend_writer.TFDSBackendWriter(
        data_directory=FLAGS.trajectories_dir,
        split_name='train',
        max_episodes_per_file=500,
        ds_config=dataset_config),
  )

A full example is available here random_agent_catch.py

Note: If you are installing Envlogger via pip, remember to install the extra dependencies:

pip install envlogger[tfds]

Reading stored trajectories

Reader can read stored trajectories. Example:

from envlogger import reader

with reader.Reader(
    data_directory='/tmp/experiment_logs') as r:
  for episode in r.episodes:
    for step in episode:
       # step is a step_data.StepData.
       # Use step.timestep.observation, step.timestep.reward, step.action etc...

Reading the dataset with TFDS/RLDS

Datasets generated with Envlogger are compatible with RLDS.

If you used the TFDS backend, you can read your data directly with tfds.builder_from_directory. Check the RLDS website for colabs and tools to manipulate your datasets.

Otherwise, you can transform them into a TFDS compatible dataset (learn how in the RLDS documentation).

Getting Started

EnvLogger currently only supports Linux based OSes and Python 3.

You can install EnvLogger via pip:

pip install envlogger

If you want to use the TFDS backend, you need to install the package with extra dependencies:

pip install envlogger[tfds]

Compiling from source

For this option you will need to install Bazel and GMP (libgmp-dev on Debian-based systems). Please note that Bazel versions >4.0 are not supported. Our recommended version is 3.7.2. Then:

git clone https://github.com/deepmind/envlogger/
cd envlogger
bazel test --test_output=errors envlogger/...

Running inside Docker

We provide a Docker image that can be used to run tests in a more controlled environment. You can run it as follows:

sh docker/build.sh
docker run -it envlogger bash
bazel test --test_output=errors envlogger/...

Benchmarks

Wrapping your environment with EnvLogger adds an approximately 2 millisecond overhead per environment step. See the following difference in distributions in the case of random agent on a 100 steps per second Catch environment (measured in milliseconds).

Percentiles	50th	95th	99th	99.9th	mean (± std)
w/o EnvLogger	10.15	10.23	11.51	14.70	10.19 (± 1.06)
w/ EnvLogger	12.65	14.39	15.94	19.43	12.88 (± 0.34)

Acknowledgements

We greatly appreciate all the support from the TF-Agents team in setting up building and testing for EnvLogger.