Unity ML-Agents | Pretrain an LLM from Scratch with Sentence Transformers| Part 2
0*Welcome back to our Tau LLM series! π*
In this episode, we're taking our project to the next level with some exciting new developments. Our highlights include:
**Project Setup**: Learn how to create a new Unity project and import the ML-Agents package.
**Agent Configuration**: Discover how to configure your agent to handle text-based inputs and outputs.
**Training the Agent**: See how we use a Python script to encode and decode sentences, and how we train the agent to generate appropriate responses.
**Scoring and Rewards**: Understand our approach to scoring the agentβs outputs and providing partial rewards to guide its learning process.
**Vector Database Implementation**: Watch as we build a custom vector database using C# and Unity's Burst Compiler and Job System. This database will store word embeddings and enable efficient similarity searches to improve our chatbot's response generation.
**Testing and Iteration**: Observe our testing process in different scenarios and how we refine the agent's behavior for better performance.
Whether youβre a beginner or an experienced developer, this project will give you valuable insights into combining Unity, ML-Agents, and NLP techniques to build a powerful chatbot.