End-to-end computation graphs for RL


This blogpost introduces version 0.3 of the TensorForce reinforcement learning (RL) library, and motivates major design changes. Development was guided by the aim to provide a better interface and implementation for optimization modules, and later execution. More generally, with version 0.3, we are taking a big step towards RL models as pure TensorFlow objects, including all control-flow. Read more

TensorForce: A TensorFlow library for applied reinforcement learning


This blogpost will give an introduction to the architecture and ideas behind TensorForce, a new reinforcement learning API built on top of TensorFlow. This post is about a practical question: How can the applied reinforcement learning community move from collections of scripts and individual examples closer to an API for reinforcement learning (RL) — a ‘tf-learn’ or ‘skikit-learn’ for RL? Before discussing the TensorForce framework, we will discuss observations and thoughts that motivated the project. Feel free to skip this part if you are just interested in the API walkthrough. We want to emphasize that this post does… Read more