Learning Human Objectives by Evaluating Hypothetical Behavior
This addresses the challenge of safely learning human objectives in reinforcement learning with minimal user queries, which is incremental as it builds on prior interactive reward learning methods.
The paper tackles the problem of aligning agent behavior with user objectives in reinforcement learning with unknown dynamics, rewards, and unsafe states, by proposing ReQueST, an algorithm that synthesizes hypothetical behaviors to query users for reward labels, resulting in significantly outperforming prior methods in reward model transfer to new environments and safely detecting unsafe states.
We seek to align agent behavior with a user's objectives in a reinforcement learning setting with unknown dynamics, an unknown reward function, and unknown unsafe states. The user knows the rewards and unsafe states, but querying the user is expensive. To address this challenge, we propose an algorithm that safely and interactively learns a model of the user's reward function. We start with a generative model of initial states and a forward dynamics model trained on off-policy data. Our method uses these models to synthesize hypothetical behaviors, asks the user to label the behaviors with rewards, and trains a neural network to predict the rewards. The key idea is to actively synthesize the hypothetical behaviors from scratch by maximizing tractable proxies for the value of information, without interacting with the environment. We call this method reward query synthesis via trajectory optimization (ReQueST). We evaluate ReQueST with simulated users on a state-based 2D navigation task and the image-based Car Racing video game. The results show that ReQueST significantly outperforms prior methods in learning reward models that transfer to new environments with different initial state distributions. Moreover, ReQueST safely trains the reward model to detect unsafe states, and corrects reward hacking before deploying the agent.