Zero-Shot Policy Transfer with Disentangled Attention
Domain adaptation is an open problem in deep reinforcement learning (RL). Often, agents are asked to perform in environments where data is difficult to obtain. In such settings, agents are trained in similar environments, such as simulators, and are then transferred to the original environment. The gap between visual observations of the source and target environments often causes the agent to fail in the target environment. We present a new RL agent, SADALA (Soft Attention DisentAngled representation Learning Agent). SADALA first learns a compressed state representation. It then jointly learns to ignore distracting features and solve the task presented. SADALA*s separation of important and unimportant visual features leads to robust domain transfer. SADALA outperforms both prior disentangled-representation based RL and domain randomization approaches across RL environments (Visual Cartpole and DeepMind Lab).
The paper proposes adding an attention mechanism to the DARLA beta-VAE approach to transfer learning. The beta-VAE, soft attention and policy are trained on appropriate source tasks and evaluated zero-shot on target tasks, using two more difficult continuous control domains with RGB observations. Results indicate some improvements to compared to the immediate relevant baseline which may be statistically significant, but it is not clear whether over 10% in practice. I cannot at this stage recommend acceptance for the following reasons: 1) The paper augments an existing method with a well understood attention mechanism, so the novelty of the approach is relatively low. 2) The experimental results are interesting, but I don*t find them compelling enough to recommend acceptance based on the results alone. The paper does not solve a major problem with the approach it is based on. In fact, the improvement seems to be smaller when the environment is more complex. 3) Several baselines which are cited in the paper are actually missing in the experiments, so it is hard to determine how important is that roughly 10% improvement compared to SOTA.
You are a member of the scientific community tasked with peer review. Review the following paper title and abstract.
### Title + Abstract
Zero-Shot Policy Transfer with Disentangled Attention
Domain adaptation is an open problem in deep reinforcement learning (RL). Often, agents are asked to perform in environments where data is difficult to obtain. In such settings, agents are trained in similar environments, such as simulators, and are then transferred to the original environment. The gap between visual observations of the source and target environments often causes the agent to fail in the target environment. We present a new RL agent, SADALA (Soft Attention DisentAngled representation Learning Agent). SADALA first learns a compressed state representation. It then jointly learns to ignore distracting features and solve the task presented. SADALA*s separation of important and unimportant visual features leads to robust domain transfer. SADALA outperforms both prior disentangled-representation based RL and domain randomization approaches across RL environments (Visual Cartpole and DeepMind Lab).
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 proposes adding an attention mechanism to the DARLA beta-VAE approach to transfer learning. The beta-VAE, soft attention and policy are trained on appropriate source tasks and evaluated zero-shot on target tasks, using two more difficult continuous control domains with RGB observations. Results indicate some improvements to compared to the immediate relevant baseline which may be statistically significant, but it is not clear whether over 10% in practice. I cannot at this stage recommend acceptance for the following reasons: 1) The paper augments an existing method with a well understood attention mechanism, so the novelty of the approach is relatively low. 2) The experimental results are interesting, but I don*t find them compelling enough to recommend acceptance based on the results alone. The paper does not solve a major problem with the approach it is based on. In fact, the improvement seems to be smaller when the environment is more complex. 3) Several baselines which are cited in the paper are actually missing in the experiments, so it is hard to determine how important is that roughly 10% improvement compared to SOTA.
assistant
The paper proposes adding an attention mechanism to the DARLA beta-VAE approach to transfer learning. The beta-VAE, soft attention and policy are trained on appropriate source tasks and evaluated zero-shot on target tasks, using two more difficult continuous control domains with RGB observations. Results indicate some improvements to compared to the immediate relevant baseline which may be statistically significant, but it is not clear whether over 10% in practice. I cannot at this stage recommend acceptance for the following reasons: 1) The paper augments an existing method with a well understood attention mechanism, so the novelty of the approach is relatively low. 2) The experimental results are interesting, but I don*t find them compelling enough to recommend acceptance based on the results alone. The paper does not solve a major problem with the approach it is based on. In fact, the improvement seems to be smaller when the environment is more complex. 3) Several baselines which are cited in the paper are actually missing in the experiments, so it is hard to determine how important is that roughly 10% improvement compared to SOTA.