Alectors: NLP Reinforcement Learning with Transformers

Alectors is a new library for natural language reinforcement learning, utilizing transformers and pre-trained encoders for decision-making capabilities.

A new library for natural language reinforcement learning, called Alectors, has been created. It utilizes a combination of pre-trained encoders (like BERT) and transformer architectures to parse embeddings from token sequences.

The library enables the use of natural language in environments, allowing for decision-making capabilities that current NLP models lack. Key features include:

• Customizable number of actions for training on different language environments and tasks
• Compatibility with various reinforcement learning agents (e.g., PPO)
• Utilization of transformer blocks with self-attention for parsing embeddings

Explore the code and documentation to learn more about Alectors and its potential applications.

Tags: natural language reinforcement learning, transformers, NLP, reinforcement learning, Alectors, BERT, PPO