reco-gym
RL Environment
A reinforcement learning environment designed to model user traffic patterns and recommendation systems in online advertising.
Code for reco-gym: A Reinforcement Learning Environment for the problem of Product Recommendation in Online Advertising
469 stars
26 watching
99 forks
Language: Jupyter Notebook
last commit: over 3 years ago
Linked from 1 awesome list
criteoopenai-gympytorchrecommendation-system
Related projects:
Repository | Description | Stars |
---|---|---|
| A collection of standardized environments and datasets for training and benchmarking algorithms in offline reinforcement learning | 1,371 |
| Provides a unified toolkit for constructing, computing, and optimizing intrinsic reward modules in reinforcement learning | 373 |
| A high-throughput reinforcement learning library with optimized synchronous and asynchronous implementations of policy gradients. | 839 |
| A collection of reinforcement learning algorithms and tools for training agents in complex environments. | 43 |
| A high-performance implementation of reinforcement learning training pipelines using JAX and PyTorch-like functionality | 755 |
| A modular reinforcement learning library with support for various environments and frameworks | 588 |
| An open-source deep reinforcement learning library that supports offline and online training, providing an intuitive API for practitioners and researchers. | 1,349 |
| Provides benchmarking policies and datasets for offline reinforcement learning | 85 |
| A Matlab implementation of a recurrent reinforcement learning algorithm for training models to make predictions or take actions in dynamic environments. | 47 |
| A toolkit for developing and evaluating reinforcement learning algorithms in a reproducible manner | 1,893 |
| A Python library for offline reinforcement learning, evaluation, and policy selection in various environments. | 117 |
| A framework that accelerates RL environment processes by leveraging JAX and GPU acceleration | 669 |
| An open-source C++ library for training reinforcement learning policies and controlling continuous-control environments. | 680 |
| A modular toolkit for rapid prototyping of reinforcement learning algorithms | 373 |
| A Python library implementing state-of-the-art deep reinforcement learning algorithms for Keras and OpenAI Gym environments. | 8 |