Irrationality can help reward inference
Specifying reward functions is difficult, which motivates the area of reward inference: learning rewards from human behavior. The starting assumption in the area is that human behavior is optimal given the desired reward function, but in reality people have many different forms of irrationality, from noise to myopia to risk aversion and beyond. This fact seems like it will be strictly harmful to reward inference: it is already hard to infer the reward from rational behavior, and noise and systematic biases make actions have less direct of a relationship to the reward. Our insight in this work is that, contrary to expectations, irrationality can actually help rather than hinder reward inference. For some types and amounts of irrationality, the expert now produces more varied policies compared to rational behavior, which help disambiguate among different reward parameters -- those that otherwise correspond to the same rational behavior. We put this to the test in a systematic analysis of the effect of irrationality on reward inference. We start by covering the space of irrationalities as deviations from the Bellman update, simulate expert behavior, and measure the accuracy of inference to contrast the different types and study the gains and losses. We provide a mutual information-based analysis of our findings, and wrap up by discussing the need to accurately model irrationality, as well as to what extent we might expect (or be able to train) real people to exhibit helpful irrationalities when teaching rewards to learners.
*First of all, TBH, I*m not very familiar with the field of reward inference / inverse reinforcement learning, my assessment to the paper is only based on educational guess.* The paper proposes a surprising insight that reasoning about irrationality of human experts could help better reward inference, which is trying to get the reward function from human expert demonstrations. The authors start from Bellman update and try to add different modifications to the Bellman update to see how different types and levels of rationality affect the reward inference. The proposed modifications include adding Boltzmann sampling instead of taking a max, tuning different levels of optimism and pessimism, transforming the reward in specific way, change the discount into myopic discount or into hyperbolic discounting, and so on. The authors conduct an experiment on a small 5x5 gridworld and find that most changes to introduce irrationality into the Bellman update have led to lower log loss than the rational expert, which demonstrates that the proposed irrationality methods are effective on reward inference. In Figure 4 and Figure 5, the authors show a few specific examples on how different types of irrationality and different levels of them could affect the policy*s behavior. The major limitation of the paper (the authors also mention it in Page 8) is that the proposed method is quite hard to generalize into many environments. The usefulness of the irrationality depends a lot on the environments and the authors may have to conduct more extensive experiments to make their claims solid.
You are a member of the scientific community tasked with peer review. Review the following paper title and abstract.
### Title + Abstract
Irrationality can help reward inference
Specifying reward functions is difficult, which motivates the area of reward inference: learning rewards from human behavior. The starting assumption in the area is that human behavior is optimal given the desired reward function, but in reality people have many different forms of irrationality, from noise to myopia to risk aversion and beyond. This fact seems like it will be strictly harmful to reward inference: it is already hard to infer the reward from rational behavior, and noise and systematic biases make actions have less direct of a relationship to the reward. Our insight in this work is that, contrary to expectations, irrationality can actually help rather than hinder reward inference. For some types and amounts of irrationality, the expert now produces more varied policies compared to rational behavior, which help disambiguate among different reward parameters -- those that otherwise correspond to the same rational behavior. We put this to the test in a systematic analysis of the effect of irrationality on reward inference. We start by covering the space of irrationalities as deviations from the Bellman update, simulate expert behavior, and measure the accuracy of inference to contrast the different types and study the gains and losses. We provide a mutual information-based analysis of our findings, and wrap up by discussing the need to accurately model irrationality, as well as to what extent we might expect (or be able to train) real people to exhibit helpful irrationalities when teaching rewards to learners.
user
<think>Okay, to do a decent peer review, I should start with summarizing what the work is about. This includes novelty, contributions, clarity, and impact. I might include suggestions to issues that I find critical to the usefulness of the work. Starting with novelty, there are different types of novelty. The first aspect of novelty is scope and scale. Is the work covering a broader perspective than previous works? The second aspect is methodology. Is the proposed methodology novel compared to previous methods? Do they provide any significant advantage over previous methodologies? The third aspect is results. Are the results different compared previous work? Does this lead to any novel theories or an extension of current theories? Next is clarity, I will be reading the work in depth and analyze if any sentences can be potentially ambiguous or misleading. If any sentences are ambiguous, I will state them and give a suggestion on how to rephrase such sentence or how to make more clear to the audience. Lastly, impact is important. I will attempt to predict how such work will impact the scientific community. This might include the applications of such work and how the work might transform the way we study a certain topic. Finally, I will might only mention issues that are critical. This is to keep the review as concise and helpful as possible.</think>



*First of all, TBH, I*m not very familiar with the field of reward inference / inverse reinforcement learning, my assessment to the paper is only based on educational guess.* The paper proposes a surprising insight that reasoning about irrationality of human experts could help better reward inference, which is trying to get the reward function from human expert demonstrations. The authors start from Bellman update and try to add different modifications to the Bellman update to see how different types and levels of rationality affect the reward inference. The proposed modifications include adding Boltzmann sampling instead of taking a max, tuning different levels of optimism and pessimism, transforming the reward in specific way, change the discount into myopic discount or into hyperbolic discounting, and so on. The authors conduct an experiment on a small 5x5 gridworld and find that most changes to introduce irrationality into the Bellman update have led to lower log loss than the rational expert, which demonstrates that the proposed irrationality methods are effective on reward inference. In Figure 4 and Figure 5, the authors show a few specific examples on how different types of irrationality and different levels of them could affect the policy*s behavior. The major limitation of the paper (the authors also mention it in Page 8) is that the proposed method is quite hard to generalize into many environments. The usefulness of the irrationality depends a lot on the environments and the authors may have to conduct more extensive experiments to make their claims solid.
assistant
*First of all, TBH, I*m not very familiar with the field of reward inference / inverse reinforcement learning, my assessment to the paper is only based on educational guess. * The paper proposes a surprising insight that reasoning about irrationality of human experts could help better reward inference, which is trying to get the reward function from human expert demonstrations. The authors start from Bellman update and try to add different modifications to the Bellman update to see how different types and levels of rationality affect the reward inference. The proposed modifications include adding Boltzmann sampling instead of taking a max, tuning different levels of optimism and pessimism, transforming the reward in specific way, change the discount into myopic discount or into hyperbolic discounting, and so on. The authors conduct an experiment on a small 5x5 gridworld and find that most changes to introduce irrationality into the Bellman update have led to lower log loss than the rational expert, which demonstrates that the proposed irrationality methods are effective on reward inference. In Figure 4 and Figure 5, the authors show a few specific examples on how different types of irrationality and different levels of them could affect the policy*s behavior. The major limitation of the paper (the authors also mention it in Page 8) is that the proposed method is quite hard to generalize into many environments. The usefulness of the irrationality depends a lot on the environments and the authors may have to conduct more extensive experiments to make their claims solid.