Tensorforce
A modular TensorFlow library for applied reinforcement learning with a focus on flexible design and practical usability.
What is Tensorforce?
Tensorforce is a TensorFlow-based deep reinforcement learning framework designed for applied use in research and practice. It provides a modular library of RL components that can be flexibly combined to build and train agents for various environments, solving problems like game playing, autonomous driving, and control tasks. The framework separates algorithms from environment specifics, enabling portable and reusable models.
Researchers and practitioners working on applied reinforcement learning projects who need a flexible, modular library to experiment with and deploy RL agents. It suits those integrating RL with simulators like OpenAI Gym, CARLA, or ViZDoom.
Developers choose Tensorforce for its emphasis on modularity and configurability, allowing easy customization of RL components without being locked into specific algorithms. Its full TensorFlow implementation ensures portable computation graphs and deployment flexibility, distinguishing it from more rigid frameworks.
Overview
Tensorforce: a TensorFlow library for applied reinforcement learning
Use Cases
Best For
- Building custom reinforcement learning agents with modular components
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