Learning and Planning with a Semantic Model
Building deep reinforcement learning agents that can generalize and adapt to unseen environments remains a fundamental challenge for AI. This paper describes progresses on this challenge in the context of man-made environments, which are visually diverse but contain intrinsic semantic regularities. We propose a hybrid model-based and model-free approach, LEArning and Planning with Semantics (LEAPS), consisting of a multi-target sub-policy that acts on visual inputs, and a Bayesian model over semantic structures. When placed in an unseen environment, the agent plans with the semantic model to make high-level decisions, proposes the next sub-target for the sub-policy to execute, and updates the semantic model based on new observations. We perform experiments in visual navigation tasks using House3D, a 3D environment that contains diverse human-designed indoor scenes with real-world objects. LEAPS outperforms strong baselines that do not explicitly plan using the semantic content.
 The contributions of this paper are in the area of semantic modelling, where the authors propose an approach called LEAPS consisting of a multi-target sub-policy that acts on visual inputs, and a Bayesian model over semantic structures. The fundamental premise of the proposed approach is that when placed in an unseen environment the agent plans with the semantic model based on new observations. Particularly, the authors propose to learn a Bayesian model over the semantic level and infer the posterior structure via the Bayes rule. The proposed approach is validated with experiments in visual navigation tasks using a 3D environment that contains diverse human-designed indoor scenes with real world objects. Finally, the authors show the key role of using semantic context compared to the baselines that do not consider semantic context. The parer is interesting, well structured and and clearly written. Also, the addressed topic of incorporating semantic model in the context of learning and planning is very interesting. The related work is extensively presented with pertinent and up-to-date literature. Furthermore, the background section presents well the DRL notations. In section 5, how the values for e.g between dinning room and garage 0.05, dinning room and kitchen 0.7 are learned, and how generalisable is this approach to other applications - because the way those priors are determined do not seem very explicit? Furthermore in the experiments it does not seem explicit how the semantic model is updated in light of new information, I think this deserves further explanation or to be clearly pinpointed? Also, what are the key requirements that make the semantic model interpretable. Because, the way the validation is conducted in this paper, it seems that ithe nterpretability is quite specific to House3D - is it generalisable to other applications and under which conditions? Otherwise, I believe that the questions asked in the experiments section are well answered with the experimental results
You are a member of the scientific community tasked with peer review. Review the following paper title and abstract.
### Title + Abstract
Learning and Planning with a Semantic Model
Building deep reinforcement learning agents that can generalize and adapt to unseen environments remains a fundamental challenge for AI. This paper describes progresses on this challenge in the context of man-made environments, which are visually diverse but contain intrinsic semantic regularities. We propose a hybrid model-based and model-free approach, LEArning and Planning with Semantics (LEAPS), consisting of a multi-target sub-policy that acts on visual inputs, and a Bayesian model over semantic structures. When placed in an unseen environment, the agent plans with the semantic model to make high-level decisions, proposes the next sub-target for the sub-policy to execute, and updates the semantic model based on new observations. We perform experiments in visual navigation tasks using House3D, a 3D environment that contains diverse human-designed indoor scenes with real-world objects. LEAPS outperforms strong baselines that do not explicitly plan using the semantic content.
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 contributions of this paper are in the area of semantic modelling, where the authors propose an approach called LEAPS consisting of a multi-target sub-policy that acts on visual inputs, and a Bayesian model over semantic structures. The fundamental premise of the proposed approach is that when placed in an unseen environment the agent plans with the semantic model based on new observations. Particularly, the authors propose to learn a Bayesian model over the semantic level and infer the posterior structure via the Bayes rule. The proposed approach is validated with experiments in visual navigation tasks using a 3D environment that contains diverse human-designed indoor scenes with real world objects. Finally, the authors show the key role of using semantic context compared to the baselines that do not consider semantic context. The parer is interesting, well structured and and clearly written. Also, the addressed topic of incorporating semantic model in the context of learning and planning is very interesting. The related work is extensively presented with pertinent and up-to-date literature. Furthermore, the background section presents well the DRL notations. In section 5, how the values for e.g between dinning room and garage 0.05, dinning room and kitchen 0.7 are learned, and how generalisable is this approach to other applications - because the way those priors are determined do not seem very explicit? Furthermore in the experiments it does not seem explicit how the semantic model is updated in light of new information, I think this deserves further explanation or to be clearly pinpointed? Also, what are the key requirements that make the semantic model interpretable. Because, the way the validation is conducted in this paper, it seems that ithe nterpretability is quite specific to House3D - is it generalisable to other applications and under which conditions? Otherwise, I believe that the questions asked in the experiments section are well answered with the experimental results
assistant
 The contributions of this paper are in the area of semantic modelling, where the authors propose an approach called LEAPS consisting of a multi-target sub-policy that acts on visual inputs, and a Bayesian model over semantic structures. The fundamental premise of the proposed approach is that when placed in an unseen environment the agent plans with the semantic model based on new observations. Particularly, the authors propose to learn a Bayesian model over the semantic level and infer the posterior structure via the Bayes rule. The proposed approach is validated with experiments in visual navigation tasks using a 3D environment that contains diverse human-designed indoor scenes with real world objects. Finally, the authors show the key role of using semantic context compared to the baselines that do not consider semantic context. The parer is interesting, well structured and and clearly written. Also, the addressed topic of incorporating semantic model in the context of learning and planning is very interesting. The related work is extensively presented with pertinent and up-to-date literature. Furthermore, the background section presents well the DRL notations. In section 5, how the values for e.g between dinning room and garage 0.05, dinning room and kitchen 0.7 are learned, and how generalisable is this approach to other applications - because the way those priors are determined do not seem very explicit? Furthermore in the experiments it does not seem explicit how the semantic model is updated in light of new information, I think this deserves further explanation or to be clearly pinpointed? Also, what are the key requirements that make the semantic model interpretable. Because, the way the validation is conducted in this paper, it seems that ithe nterpretability is quite specific to House3D - is it generalisable to other applications and under which conditions? Otherwise, I believe that the questions asked in the experiments section are well answered with the experimental results