Enabling Environment Design via Active Indirect Elicitation

Many situations arise in which an interested party wishes to affect the decisions of an agent; e.g., a teacher that seeks to promote particular study habits, a Web 2.0 site that seeks to encourage users to contribute content, or an online retailer that seeks to encourage consumers to write reviews. In the problem of environment design, one assumes an interested party who is able to alter limited aspects of the environment for the purpose of promoting desirable behaviors. A critical aspect of environment design is understanding preferences, but by assumption direct queries are unavailable. We work in the inverse reinforcement learning framework, adopting here the idea of active indirect preference elicitation to learn the reward function of the agent by observing behavior in response to incentives. We show that the process is convergent and obtain desirable bounds on the number of elicitation rounds. We briefly discuss generalizations of the elicitation method to other forms of environment design, e.g., modifying the state space, transition model, and available actions.Engineering and Applied Science

Value-Based Policy Teaching with Active Indirect Elicitation

Many situations arise in which an interested party's utility is dependent on the actions of an agent; e.g., a teacher is interested in a student learning effectively and a firm is interested in a consumer's behavior. We consider an environment in which the interested party can provide incentives to affect the agent's actions but cannot otherwise enforce actions. In value-based policy teaching, we situate this within the framework of sequential decision tasks modeled by Markov Decision Processes, and seek to associate limited rewards with states that induce the agent to follow a policy that maximizes the total expected value of the interested party. We show value-based policy teaching is NP-hard and provide a mixed integer program formulation. Focusing in particular on environments in which the agent's reward is unknown to the interested party, we provide a method for active indirect elicitation wherein the agent's reward function is inferred from observations about its response to incentives. Experimental results suggest that we can generally find the optimal incentive provision in a small number of elicitation rounds.Engineering and Applied Science

Efficiently Finding Approximately-Optimal Queries for Improving Policies and Guaranteeing Safety

When a computational agent (called the “robot”) takes actions on behalf of a human user, it may be uncertain about the human’s preferences. The human may initially specify her preferences incompletely or inaccurately. In this case, the robot’s performance may be unsatisfactory or even cause negative side effects to the environment. There are approaches in the literature that may solve this problem. For example, the human can provide some demonstrations which clarify the robot’s uncertainty. The human may give real-time feedback to the robot’s behavior, or monitor the robot and stop the robot when it may perform anything dangerous. However, these methods typically require much of the human’s attention. Alternatively, the robot may estimate the human’s true preferences using the specified preferences, but this is error-prone and requires making assumptions on how the human specifies her preferences. In this thesis, I consider a querying approach. Before taking any actions, the robot has a chance to query the human about her preferences. For example, the robot may query the human about which trajectory in a set of trajectories she likes the most, or whether the human cares about some side effects to the domain. After the human responds to the query, the robot expects to improve its performance and/or guarantee that its behavior is considered safe by the human. If we do not impose any constraint on the number of queries the robot can pose, the robot may keep posing queries until it is absolutely certain about the human’s preferences. This may consume too much of the human’s cognitive load. The information obtained in the responses to some of the queries may only marginally improve the robot’s performance, which is not worth the human’s attention at all. So in the problems considered in this thesis, I constrain the number of queries that the robot can pose, or associate each query with a cost. The research question is how to efficiently find the most useful query under such constraints. Finding a provably optimal query can be challenging since it is usually a combinatorial optimization problem. In this thesis, I contribute to providing efficient query selection algorithms under uncertainty. I first formulate the robot’s uncertainty as reward uncertainty and safety-constraint uncertainty. Under only reward uncertainty, I provide a query selection algorithm that finds approximately-optimal k-response queries. Under only safety-constraint uncertainty, I provide a query selection algorithm that finds an optimal k-element query to improve a known safe policy, and an algorithm that uses a set-cover-based query selection strategy to find an initial safe policy. Under both types of uncertainty simultaneously, I provide a batch-query-based querying method that empirically outperforms other baseline querying methods.PHDComputer Science & EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/163125/1/shunzh_1.pd