/ Machine Learning

investigate core capabilities of a reinforcement learning (RL) agent

investigate core capabilities of a reinforcement learning (RL) agent

Behaviour Suite for Reinforcement Learning (bsuite)

bsuite is a collection of carefully-designed experiments that investigate core capabilities of a reinforcement learning (RL) agent with two main objectives.

  1. To collect clear, informative and scalable problems that capture key issues
    in the design of efficient and general learning algorithms.
  2. To study agent behavior through their performance on these shared
    benchmarks.

This library automates evaluation and analysis of any agent on these benchmarks.
It serves to facilitate reproducible, and accessible, research on the core
issues in RL, and ultimately the design of superior learning algorithms.

Going forward, we hope to incorporate more excellent experiments from the
research community, and commit to a periodic review of the experiments from a
committee of prominent researchers.

For a more comprehensive overview, see the accompanying [paper].

Technical overview

bsuite is a collection of experiments, defined in the [experiments]
subdirectory. Each subdirectory corresponds to one experiment and contains:

  • A file defining an RL environment, which may be configurable to provide
    different levels of difficulty or different random seeds (for example).
  • A sequence of keyword arguments for this environment, defined in the
    SETTINGS variable found in the experiment's sweep.py file.
  • A file analysis.py defining plots used in the provided Jupyter notebook.

bsuite works by logging results from "within" each environment, when loading
environment via a
load_and_record* function.
This means any experiment will automatically output data in the correct format
for analysis using the notebook, without any constraints on the structure of
agents or algorithms.

Getting started

If you are new to bsuite you can get started in our colab tutorial.
This Jupyter notebook is hosted with a free cloud server, so you can start
coding right away without installing anything on your machine. After this,
you can follow the instructions below to get bsuite running on your local machine.

Installation

We have tested bsuiteon Python 3.6. We do not attempt to maintain a working
version for Python 2.7.

To install bsuite, run the command

pip install git+git://github.com/deepmind/bsuite.git

or clone the repository and run

pip install bsuite/

To install the package while being able to edit the code (see baselines below),
run

pip install -e bsuite/

To also install dependencies for the baselines/ examples (excluding Gym and
Dopamine examples), install with:

pip install -e bsuite[baselines]

Loading an environment

Environments are specified by a bsuite_id string, for example "deep_sea/7".
This string denotes the experiment and the (index of the) environment settings
to use, as described in the technical overview section.

import bsuite

env = bsuite.load_from_id('catch/0')

The sequence of bsuite_ids required to run all experiments can be accessed
programmatically via:

from bsuite import sweep

sweep.SWEEP

This module also contains bsuite_ids for each experiment individually via
uppercase constants corresponding to the experiment name, for example:

sweep.DEEP_SEA
sweep.DISCOUNTING_CHAIN

Loading an environment with logging included

We include two implementations of automatic logging, available via:

  • [bsuite.load_and_record_to_csv]. This outputs one CSV file per
    bsuite_id, so is suitable for running a set of bsuite experiments split
    over multiple machines. The implementation is in [logging/csv_logging.py]
  • [bsuite.load_and_record_to_sqlite]. This outputs a single file, and is
    best suited when running a set of bsuite experiments via multiple processes
    on a single workstation. The implementation is in
    [logging/sqlite_logging.py].

We also include a terminal logger in [logging/terminal_logging.py], exposed
via bsuite.load_and_record_to_terminal.

It is easy to write your own logging mechanism, if you need to save results to a
different storage system. See the CSV implementation for the simplest reference.

Interacting with an environment

Our environments implement the Python interface defined in
dm_env.

More specifically, all our environments accept a discrete, zero-based integer
action (or equivalently, a scalar numpy array with shape ()).

To determine the number of actions for a specific environment, use

num_actions = env.action_spec().num_values

Each environment returns observations in the form of a numpy array.

We also expose a bsuite_num_episodes property for each environment in bsuite.
This allows users to run exactly the number of episodes required for bsuite's
analysis, which may vary between environments used in different experiments.

Example run loop for a hypothetical agent with a step() method.

for _ in range(env.bsuite_num_episodes):
  timestep = env.reset()
  while not timestep.last():
    action = agent.step(timestep)
    timestep = env.step(action)
  agent.step(timestep)

Using bsuite with OpenAI Gym

To use bsuite with a codebase that uses the
OpenAI Gym interface, use the GymWrapper
class in [utils/gym_wrapper.py]:

import bsuite
from bsuite.utils import gym_wrapper

env = bsuite.load_and_record_to_csv('catch/0', results_dir='/path/to/results')
gym_env = gym_wrapper.GymWrapper(env)

Note that bsuite does not include Gym in its default dependencies, so you may
need to pip install it separately.

Environments

These environments all have small observation sizes such that you should expect
reasonable performance running with a small network in a single process on CPU.

Complete descriptions of each environment and their corresponding experiments
are found in the [analysis/results.ipynb] Jupyter notebook.

Baseline agents

We include implementations of several common agents in the baselines
subdirectory, along with a minimal run-loop.

See the installation section for how to include the required
dependencies (mainly TensorFlow and
Sonnet) at install time. These
dependencies are not installed by default, since bsuite does not require users
to use any specific machine learning library.

Running the entire suite of experiments

Each of the agents in the baselines folder contains a run script which
serves as an example which can run against a single environment or against the
entire suite of experiments, by passing the --bsuite_id=SWEEP flags; this will
start a pool of processes with which to run as many experiments in parallel as
the host machine allows. On a 12 core machine, this will complete overnight for
most agents. Alternatively, it is possible to run on Google Compute Platform
using run_on_gcp.sh, steps of which are outlined below.

Running experiments on Google Cloud Platform

run_on_gcp.sh does the following in order:

  1. Create an instance with specified specs (by default 64-core CPU optimized).
  2. git clones bsuite and installs it together with other dependencies.
  3. Runs the specified agent (currently limited to /baselines) on a specified
    environment.
  4. Copies the resulting SQLite file to /tmp/bsuite.db from the remote
    instance to you local machine.
  5. Shuts down the created instance.

In order to run the script, you first need to create a billing account. Then
follow the instructions
here to setup and
initialize Cloud SDK. After completing gcloud init, you are ready to run
bsuite on Google Cloud.

For this make run_on_gcp.sh executable and run it:

chmod +x run_on_gcp.sh
./run_on_gcp.sh

After the instance is created, the instance name will be printed. Then you can
ssh into the instance by selecting Compute Engine -> Instances and clicking
SSH. Note that this is not necessary, as the result will be copied to your
local machine once it is ready. However, sshing might be convenient if you
want to make local changes to agent and environments. In this case, after
sshing, do

~/bsuite_env/bin/activate

to activate the virtual environment. Then you can run agents via

python ~/bsuite/bsuite/baselines/dqn/run.py --bsuite_id=SWEEP

for instance.

Analysis

bsuite comes with a ready-made analysis Jupyter notebook included in
[analysis/results.ipynb]. This notebook loads and processes logged data, and
produces the scores and plots for each experiment. We recommend using this
notebook in conjunction with Colaboratory.

We provide an example of a such bsuite report here.

bsuite Report

You can use bsuite to generate an automated 1-page appendix, that summarizes
the core capabilities of your RL algorithm. This appendix is compatible with
most major ML conference formats. For example output run,

pdflatex bsuite/reports/neurips_2019/neurips_2019.tex

More examples of bsuite reports can be found in the reports/ subdirectory.

Citing

If you use bsuite in your work, please cite the accompanying paper:

@article{osband2019bsuite,
         title={Behaviour Suite for Reinforcement Learning},
         author={Osband, Ian and
                 Doron, Yotam and
                 Hessel, Matteo and
                 Aslanides, John and
                 Sezener, Eren and
                 Saraiva, Andre and
                 McKinney, Katrina and
                 Lattimore, Tor and
                 {Sz}epesv{\'a}ri, Csaba and
                 Singh, Satinder and
                 Van Roy, Benjamin and
                 Sutton, Richard and
                 Silver, David and
                 van Hasselt, Hado},
         year={2019},
}

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