Researchers from MIT, Harvard University, and the University of Washington have developed a new approach to reinforcement learning that leverages feedback from nonexpert users to teach AI agents specific tasks. Unlike other methods, this approach enables the agent to learn more quickly despite the noisy and potentially inaccurate feedback. The method has the potential to allow robots to learn tasks without the need for physical demonstrations and can be scaled up efficiently.
New Method Uses Crowdsourced Feedback to Train AI Robots
Researchers from MIT, Harvard University, and the University of Washington have developed an innovative reinforcement learning approach that allows AI agents to learn new tasks without relying on expertly designed reward functions. Instead, the agents are guided by crowdsourced feedback from nonexpert users, which helps them reach their goals more quickly. This method is particularly useful for complex tasks that involve multiple steps.
The traditional approach to teaching AI agents involves designing a reward function, which can be time-consuming and difficult to scale. With the new method, nonexpert users can provide feedback asynchronously, making it possible for people from around the world to contribute to teaching the agents.
The researchers decoupled the process into two separate parts: a goal selector algorithm that is continuously updated with human feedback, and an agent that explores on its own, guided by the goal selector. The feedback gently guides the agent’s behavior, leading it to more promising areas closer to its goal. Even if the feedback is inaccurate, the agent can still learn to complete the task.
The method was tested on both simulated and real-world tasks, and it outperformed other methods in terms of learning speed. Crowdsourced data from nonexperts yielded better performance than synthetic data produced by researchers, and labeling images or videos took less than two minutes for nonexpert users.
The researchers are continuing to refine the method and explore its applications in teaching multiple agents at once. They emphasize the importance of ensuring that AI agents are aligned with human values.
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