What is RAG? (Reinforcement Learning with Human-AI Collaboration)

RAG (Reinforcement Learning with Human-AI Collaboration) can be applied to train agents that can generate new, diverse, and high-quality outputs, such as images, videos, music, or text. By incorporating human feedback and guidance into the training process, RAG can help generative models overcome some of their limitations, such as mode collapse or lack of diversity, and produce more creative and coherent outputs.

RAG typically involves the following four components:

1. Reinforcement learning

This involves an agent interacting with an environment to maximize a reward signal. In the context of generative AI, the agent learns to generate new outputs by trial and error, and receives rewards based on the quality and novelty of its productions.

2. Human-AI collaboration

In RAG, humans are involved in the training process, providing feedback and guidance to the agent. This can take various forms, such as rating the generated outputs, providing examples or demonstrations, or correcting the agent's mistakes.

3. Transfer learning

RAG often employs transfer learning techniques to enable the agent to learn from human experts or pre-trained models. This allows the agent to leverage existing knowledge and skills, and adapt to new tasks or environments more quickly.

4. Multi-objective optimization

RAG often involves optimizing multiple objectives simultaneously, such as diversity, novelty, and quality. This requires balancing different reward signals and exploration strategies to ensure that the agent generates a wide range of high-quality outputs.