On Inductive Biases in Deep Reinforcement Learning
Many deep reinforcement learning algorithms contain inductive biases that sculpt the agent*s objective and its interface to the environment. These inductive biases can take many forms, including domain knowledge and pretuned hyper-parameters. In general, there is a trade-off between generality and performance when we use such biases. Stronger biases can lead to faster learning, but weaker biases can potentially lead to more general algorithms that work on a wider class of problems. This trade-off is relevant because these inductive biases are not free; substantial effort may be required to obtain relevant domain knowledge or to tune hyper-parameters effectively. In this paper, we re-examine several domain-specific components that modify the agent*s objective and environmental interface. We investigated whether the performance deteriorates when all these fixed components are replaced with adaptive solutions from the literature. In our experiments, performance sometimes decreased with the adaptive components, as one might expect when comparing to components crafted for the domain, but sometimes the adaptive components performed better. We then investigated the main benefit of having fewer domain-specific components, by comparing the learning performance of the two systems on a different set of continuous control problems, without additional tuning of either system. As hypothesized, the system with adaptive components performed better on many of the tasks.
 The paper presents and evaluates different common inductive biases in Deep RL. These are systematically evaluated on different experimental settings. The paper is easy to read and the authors explain well the setting and their findings. The comparison and evaluations is well conducted and valuable contribution to the literature. I would have liked some more details on the motivating example in section 3.1, maybe with a figure supporting the explanation of the example.
You are a member of the scientific community tasked with peer review. Review the following paper title and abstract.
### Title + Abstract
On Inductive Biases in Deep Reinforcement Learning
Many deep reinforcement learning algorithms contain inductive biases that sculpt the agent*s objective and its interface to the environment. These inductive biases can take many forms, including domain knowledge and pretuned hyper-parameters. In general, there is a trade-off between generality and performance when we use such biases. Stronger biases can lead to faster learning, but weaker biases can potentially lead to more general algorithms that work on a wider class of problems. This trade-off is relevant because these inductive biases are not free; substantial effort may be required to obtain relevant domain knowledge or to tune hyper-parameters effectively. In this paper, we re-examine several domain-specific components that modify the agent*s objective and environmental interface. We investigated whether the performance deteriorates when all these fixed components are replaced with adaptive solutions from the literature. In our experiments, performance sometimes decreased with the adaptive components, as one might expect when comparing to components crafted for the domain, but sometimes the adaptive components performed better. We then investigated the main benefit of having fewer domain-specific components, by comparing the learning performance of the two systems on a different set of continuous control problems, without additional tuning of either system. As hypothesized, the system with adaptive components performed better on many of the tasks.
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>



 The paper presents and evaluates different common inductive biases in Deep RL. These are systematically evaluated on different experimental settings. The paper is easy to read and the authors explain well the setting and their findings. The comparison and evaluations is well conducted and valuable contribution to the literature. I would have liked some more details on the motivating example in section 3.1, maybe with a figure supporting the explanation of the example.
assistant
 The paper presents and evaluates different common inductive biases in Deep RL. These are systematically evaluated on different experimental settings. The paper is easy to read and the authors explain well the setting and their findings. The comparison and evaluations is well conducted and valuable contribution to the literature. I would have liked some more details on the motivating example in section 3.1, maybe with a figure supporting the explanation of the example.