TensorForce - A TensorFlow library for applied reinforcement learning
A modular TensorFlow library for applied reinforcement learning with a focus on flexible design and practical usability.
What is TensorForce - A TensorFlow library for applied reinforcement learning?
Tensorforce is a TensorFlow-based deep reinforcement learning library designed for applied use in both research and practice. It provides a modular framework where RL algorithms are separated from environment specifics, allowing developers to build and experiment with flexible models. The library supports a wide range of components like network architectures, memory types, and optimization methods to replicate popular RL algorithms.
Researchers and practitioners working on applied reinforcement learning projects who need a flexible, modular library to experiment with and deploy RL models. It's suitable for those familiar with TensorFlow and Python.
Developers choose Tensorforce for its emphasis on modularity and configurability, enabling easy combination of components to tailor models to specific problems. Its full TensorFlow integration ensures portable computation graphs and straightforward deployment, distinguishing it from more rigid RL frameworks.
Overview
Tensorforce: a TensorFlow library for applied reinforcement learning
Use Cases
Best For
- Implementing modular reinforcement learning algorithms with customizable components
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