A stable version of Tensorforce is periodically updated on PyPI and installed as follows:

pip3 install tensorforce

To always use the latest version of Tensorforce, install the GitHub version instead:

git clone
cd tensorforce
pip3 install -e .

Environments require additional packages for which there are setup options available (ale, gym, retro, vizdoom, carla; or envs for all environments), however, some require additional tools to be installed separately (see environments documentation). Other setup options include tfa for TensorFlow Addons and tune for HpBandSter required for the script.

Note on GPU usage: Different from (un)supervised deep learning, RL does not always benefit from running on a GPU, depending on environment and agent configuration. In particular for RL-typical environments with low-dimensional state spaces (i.e., no images), one usually gets better performance by running on CPU only. Consequently, Tensorforce is configured to run on CPU by default, which can be changed via the agent’s config argument, for instance, config=dict(device='GPU').

M1 Macs

At the moment Tensorflow cannot be installed on M1 Macs directly. You need to follow Apple’s guide to install tensorflow-macos instead.

Then, since Tensorforce has tensorflow as its dependency and not tensorflow-macos, you need to install all Tensorforce’s dependencies from requirements.txt manually (except for tensorflow == 2.5.0 of course).

In the end, install tensorforce while forcing pip to ignore its dependencies:

pip3 install tensorforce --no-deps


If you want to use Tensorforce within a Docker container, the following is a minimal Dockerfile to get started:

FROM python:3.8
  pip3 install tensorforce

Or alternatively for the latest version:

FROM python:3.8
  git clone && \
  pip3 install -e tensorforce

Subsequently, the container can be built via:

docker build .