Training Agent for First-Person Shooter Game with Actor-Critic Curriculum Learning
In this paper, we propose a novel framework for training vision-based agent for First-Person Shooter (FPS) Game, in particular Doom. Our framework combines the state-of-the-art reinforcement learning approach (Asynchronous Advantage Actor-Critic (A3C) model) with curriculum learning. Our model is simple in design and only uses game states from the AI side, rather than using opponents* information. On a known map, our agent won 10 out of the 11 attended games and the champion of Track1 in ViZDoom AI Competition 2016 by a large margin, 35\% higher score than the second place.
This paper basically applies A3C to 3D spatial navigation tasks. - This is not the first time A3C has been applied to 3D navigation. In fact the original paper reported these experiments. Although the experimental results are great, I am not sure if this paper has any additional insights to warrant itself as a conference paper. It might make more sense as a workshop paper - Are the graphs in Fig 5 constructed using a single hyper-parameter sweep? I think the authors should report results with many random initializations to make the comparisons more robust - Overall the two main ideas in this paper -- A3C and curriculums -- are not really novel but the authors do make use of them in a real system.
You are a member of the scientific community tasked with peer review. Review the following paper title and abstract.
### Title + Abstract
Training Agent for First-Person Shooter Game with Actor-Critic Curriculum Learning
In this paper, we propose a novel framework for training vision-based agent for First-Person Shooter (FPS) Game, in particular Doom. Our framework combines the state-of-the-art reinforcement learning approach (Asynchronous Advantage Actor-Critic (A3C) model) with curriculum learning. Our model is simple in design and only uses game states from the AI side, rather than using opponents* information. On a known map, our agent won 10 out of the 11 attended games and the champion of Track1 in ViZDoom AI Competition 2016 by a large margin, 35\% higher score than the second place.
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>



This paper basically applies A3C to 3D spatial navigation tasks. - This is not the first time A3C has been applied to 3D navigation. In fact the original paper reported these experiments. Although the experimental results are great, I am not sure if this paper has any additional insights to warrant itself as a conference paper. It might make more sense as a workshop paper - Are the graphs in Fig 5 constructed using a single hyper-parameter sweep? I think the authors should report results with many random initializations to make the comparisons more robust - Overall the two main ideas in this paper -- A3C and curriculums -- are not really novel but the authors do make use of them in a real system.
assistant
This paper basically applies A3C to 3D spatial navigation tasks. - This is not the first time A3C has been applied to 3D navigation. In fact the original paper reported these experiments. Although the experimental results are great, I am not sure if this paper has any additional insights to warrant itself as a conference paper. It might make more sense as a workshop paper - Are the graphs in Fig 5 constructed using a single hyper-parameter sweep? I think the authors should report results with many random initializations to make the comparisons more robust - Overall the two main ideas in this paper -- A3C and curriculums -- are not really novel but the authors do make use of them in a real system.