/ Machine Learning

A PyTorch Platform for Distributed RL

A PyTorch Platform for Distributed RL

TorchBeast

A PyTorch implementation of IMPALA: Scalable Distributed Deep-RL with Importance Weighted Actor-Learner Architectures by Espeholt, Soyer, Munos et al.

TorchBeast comes in two variants: MonoBeast and PolyBeast. While PolyBeast is more powerful (e.g. allowing training across machines), it's somewhat harder to install. MonoBeast requires only Python and PyTorch.

Getting started: MonoBeast

MonoBeast is a pure Python + PyTorch implementation of IMPALA.

To set it up, create a new conda environment and install MonoBeast's
requirements:

$ conda create -n torchbeast python=3.7
$ conda activate torchbeast
$ conda install pytorch -c pytorch
$ pip install -r requirements.txt

Then run MonoBeast, e.g. on the Pong Atari
environment
:

$ python -m torchbeast.monobeast --env PongNoFrameskip-v4

By default, MonoBeast uses only a few actors (each with their instance
of the environment). Let's change the default settings (try this on a
beefy machine!):

$ python -m torchbeast.monobeast \
     --env PongNoFrameskip-v4 \
     --num_actors 45 \
     --total_steps 30000000 \
     --learning_rate 0.0004 \
     --epsilon 0.01 \
     --entropy_cost 0.01 \
     --batch_size 4 \
     --unroll_length 80 \
     --num_buffers 60 \
     --num_threads 4 \
     --xpid example

Results are logged to ~/logs/torchbeast/latest and a checkpoint file is
written to ~/logs/torchbeast/latest/model.tar.

Once training finished, we can test performance on a few episodes:

$ python -m torchbeast.monobeast \
     --env PongNoFrameskip-v4 \
     --mode test \
     --xpid example

MonoBeast is a simple, single-machine version of IMPALA.
Each actor runs in a separate process with its dedicated instance of
the environment and runs the PyTorch model on the CPU to create
actions. The resulting rollout trajectories
(environment-agent interactions) are sent to the learner. In the main
process, the learner consumes these rollouts and uses them to update
the model's weights.

Faster version: PolyBeast

PolyBeast provides a faster and more scalable implementation of
IMPALA.

The easiest way to build and install all of PolyBeast's dependencies
and run it is to use Docker:

$ docker build -t torchbeast .
$ docker run --name torchbeast torchbeast

To run PolyBeast directly on Linux or MacOS, follow this guide.

Installing PolyBeast

Linux

Create a new Conda environment, and install PolyBeast's requirements:

$ conda create -n torchbeast python=3.7
$ conda activate torchbeast
$ pip install -r requirements.txt

PolyBeast requires installing PyTorch
from source.

PolyBeast also requires gRPC, which can be installed by running:

$ git submodule update --init --recursive
$ conda install -c anaconda protobuf
$ ./scripts/install_grpc.sh

Finally, let's compile the C++ parts of PolyBeast:

$ pip install nest/
$ export LD_LIBRARY_PATH=${CONDA_PREFIX:-"$(dirname $(which conda))/../"}/lib:${LD_LIBRARY_PATH}
$ python setup.py install

MacOS

Create a new Conda environment, and install PolyBeast's requirements:

$ conda create -n torchbeast python=3.7
$ conda activate torchbeast
$ pip install -r requirements.txt

On MacOS, we can use homebrew to install gRPC:

$ brew install grpc

PyTorch can be installed as per its
website (select Conda and
Python 3.7).

Compile and install the C++ parts of PolyBeast:

$ pip install nest/
$ TORCHBEAST_LIBS_PREFIX=/usr/local CXX=c++ python setup.py install

Running PolyBeast

To start both the environment servers and the learner process, run

$ python -m torchbeast.polybeast

The environment servers can also be started separately:

python -m torchbeast.polybeast_env --num_servers 10

Start another terminal and run:

$ python -m torchbeast.polybeast --no_start_servers

(Very rough) overview of the system

|-----------------|     |-----------------|                  |-----------------|
|     ACTOR 1     |     |     ACTOR 2     |                  |     ACTOR n     |
|-------|         |     |-------|         |                  |-------|         |
|       |  .......|     |       |  .......|     .   .   .    |       |  .......|
|  Env  |<-.Model.|     |  Env  |<-.Model.|                  |  Env  |<-.Model.|
|       |->.......|     |       |->.......|                  |       |->.......|
|-----------------|     |-----------------|                  |-----------------|
   ^     I                 ^     I                              ^     I
   |     I                 |     I                              |     I Actors
   |     I rollout         |     I rollout               weights|     I send
   |     I                 |     I                     /--------/     I rollouts
   |     I          weights|     I                     |              I (frames,
   |     I                 |     I                     |              I  actions
   |     I                 |     v                     |              I  etc)
   |     L=======>|--------------------------------------|<===========J
   |              |.........      LEARNER                |
   \--------------|..Model.. Consumes rollouts, updates  |
     Learner      |.........       model weights         |
      sends       |--------------------------------------|
     weights

The system has two main components, actors and a learner.

Actors generate rollouts (tensors from a number of steps of
environment-agent interactions, including environment frames, agent
actions and policy logits, and other data).

The learner consumes that experience, computes a loss and updates the
weights. The new weights are then propagated to the actors.

Learning curves on Atari

We ran TorchBeast on Atari, using the same hyperparamaters and neural
network as in the IMPALA
paper
. For comparison, we also ran
the open source TensorFlow implementation of
IMPALA
, using the same
environment
preprocessing
. The
results are equivalent; see our paper for details.

deep_network

Repository contents

libtorchbeast: C++ library that allows efficient learner-actor
communication via queueing and batching mechanisms. Some functions are
exported to Python using pybind11. For PolyBeast only.

nest: C++ library that allows to manipulate complex
nested structures. Some functions are exported to Python using
pybind11.

tests: Collection of python tests.

third_party: Collection of third-party dependencies as Git
submodules. Includes gRPC.

torchbeast: Contains monobeast.py, and polybeast.py and
polybeast_env.py.

Hyperparamaters

Both MonoBeast and PolyBeast have flags and hyperparameters. To
describe a few of them:

  • num_actors: The number of actors (and environment instances). The
    optimal number of actors depends on the capabilities of the machine
    (e.g. you would not have 100 actors on your laptop). In default
    PolyBeast this should match the number of servers started.
  • batch_size: Determines the size of the learner inputs.
  • unroll_length: Length of a rollout (i.e., number of steps that an
    actor has to be perform before sending its experience to the
    learner). Note that every batch will have dimensions
    [unroll_length, batch_size, ...].

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