What is Reinforcement Learning from Human Feedback?

What is reinforcement learning from human feedback?

Reinforcement learning from human feedback (RLHF) is a subfield of artificial intelligence (AI) that combines the power of human guidance with machine learning algorithms. It involves training an AI agent to make decisions by receiving feedback. Unlike traditional reinforcement learning (RL), where the agent learns through trial and error, RLHF enables faster and more targeted learning by leveraging human expertise.

How does RLHF work?

RLHF is an algorithm that combines the capabilities of artificial intelligence (AI) systems with human expertise to enable efficient learning of complex tasks. By incorporating feedback from human trainers, AI agents can learn effectively. Below is a detailed explanation of the RLHF process:

Step 1 – Initialization: Begin by defining the task you want the AI agent to learn and formulate an appropriate reward function.

Step 2 – Collection and preprocessing of demonstrations: Gather demonstrations from skilled human trainers who excel at the task. These demonstrations serve as valuable examples for the AI agent to learn from. Process the collected demonstrations into a suitable format for training the AI agent.

Step 3 – Initial training of the policy: Train the AI agent using the demonstrations as a starting point. The agent learns to imitate the behavior of the human trainers based on the collected data.

Step 4 – Policy iteration: Deploy the initial policy and allow the AI agent to interact with the environment. The agent's actions are determined by the policy it has learned.

Step 5 – Human feedback: Human trainers provide feedback on the agent's actions. This feedback can be in the form of binary evaluations (good or bad) or more detailed signals.

Step 6 – Earning the reward model: Utilize the human feedback to develop a reward model that captures the preferences of the trainers. This reward model assists in guiding the agent's learning process.

Step 7 – Policy update: The agent's policy incorporates the learned reward model. Through human feedback and interactions with the environment, the agent progressively enhances its performance.

Step 8 – Iterative process: Repeat steps 4 to 7 iteratively, allowing the AI agent to refine its policy based on new demonstrations and feedback.

Step 9 – Convergence: The RLHF algorithm continues until the agent's performance reaches a satisfactory level or a predetermined stopping criterion is met.

LHF combines human trainers’ expertise with AI’s learning capabilities, effectively and efficiently learning complex tasks.