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This is my school project. It focuses on Reinforcement Learning for personalized news recommendation. The main distinction is that it tries to solve online off-policy learning with dynamically generated item embeddings. I want to create a library with SOTA algorithms for reinforcement learning recommendation, providing the level of abstraction you like.
Features
- You can import the entire algorithm (say DDPG) and tell it to ddpg.learn(batch), you can import networks and the learning function separately, create a custom loader for your task, or can define everything by yourself
- Examples do not contain any of the junk code or workarounds: pure model definition and the algorithm itself in one file. I wrote a couple of articles explaining how it functions
- Documentation available
- The learning is built around sequential or frame environment that supports ML20M and like
- Seq and Frame determine the length type of sequential data, seq is fully sequential dynamic size (WIP), while the frame is just a static frame
- State Representation module with various methods. For sequential state representation, you can use LSTM/RNN/GRU (WIP)
- Parallel data loading with Modin (Dask / Ray) and caching
- Pytorch 1.7 support with Tensorboard visualization.
Project Samples
Categories
Libraries, Reinforcement Learning Frameworks, Reinforcement Learning Libraries, Reinforcement Learning AlgorithmsLicense
Apache License V2.0Follow RecNN
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