Deep Online Learning Via Meta-Learning: Continual Adaptation for Model-Based RL
Humans and animals can learn complex predictive models that allow them to accurately and reliably reason about real-world phenomena, and they can adapt such models extremely quickly in the face of unexpected changes. Deep neural network models allow us to represent very complex functions, but lack this capacity for rapid online adaptation. The goal in this paper is to develop a method for continual online learning from an incoming stream of data, using deep neural network models. We formulate an online learning procedure that uses stochastic gradient descent to update model parameters, and an expectation maximization algorithm with a Chinese restaurant process prior to develop and maintain a mixture of models to handle non-stationary task distributions. This allows for all models to be adapted as necessary, with new models instantiated for task changes and old models recalled when previously seen tasks are encountered again. Furthermore, we observe that meta-learning can be used to meta-train a model such that this direct online adaptation with SGD is effective, which is otherwise not the case for large function approximators. We apply our method to model-based reinforcement learning, where adapting the predictive model is critical for control; we demonstrate that our online learning via meta-learning algorithm outperforms alternative prior methods, and enables effective continuous adaptation in non-stationary task distributions such as varying terrains, motor failures, and unexpected disturbances.
 The authors proposed a new method to learn streaming online updates for neural networks with meta-learning and applied it to multi-task reinforcement learning. Model-agnostic meta-learning is used to learn the initial weight and task distribution is learned with the Chinese restaurant process. It sounds like an interesting idea and practical for RL. Extensive experiments show the effectiveness of the proposed method. The authors said that online updating the meta-learner did not improve the results, which is a bit surprised. Also how many data are meta-trained is not clearly described in the paper. Maybe the authors can compare the results with less data for meta-training.
You are a member of the scientific community tasked with peer review. Review the following paper title and abstract.
### Title + Abstract
Deep Online Learning Via Meta-Learning: Continual Adaptation for Model-Based RL
Humans and animals can learn complex predictive models that allow them to accurately and reliably reason about real-world phenomena, and they can adapt such models extremely quickly in the face of unexpected changes. Deep neural network models allow us to represent very complex functions, but lack this capacity for rapid online adaptation. The goal in this paper is to develop a method for continual online learning from an incoming stream of data, using deep neural network models. We formulate an online learning procedure that uses stochastic gradient descent to update model parameters, and an expectation maximization algorithm with a Chinese restaurant process prior to develop and maintain a mixture of models to handle non-stationary task distributions. This allows for all models to be adapted as necessary, with new models instantiated for task changes and old models recalled when previously seen tasks are encountered again. Furthermore, we observe that meta-learning can be used to meta-train a model such that this direct online adaptation with SGD is effective, which is otherwise not the case for large function approximators. We apply our method to model-based reinforcement learning, where adapting the predictive model is critical for control; we demonstrate that our online learning via meta-learning algorithm outperforms alternative prior methods, and enables effective continuous adaptation in non-stationary task distributions such as varying terrains, motor failures, and unexpected disturbances.
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 authors proposed a new method to learn streaming online updates for neural networks with meta-learning and applied it to multi-task reinforcement learning. Model-agnostic meta-learning is used to learn the initial weight and task distribution is learned with the Chinese restaurant process. It sounds like an interesting idea and practical for RL. Extensive experiments show the effectiveness of the proposed method. The authors said that online updating the meta-learner did not improve the results, which is a bit surprised. Also how many data are meta-trained is not clearly described in the paper. Maybe the authors can compare the results with less data for meta-training.
assistant
 The authors proposed a new method to learn streaming online updates for neural networks with meta-learning and applied it to multi-task reinforcement learning. Model-agnostic meta-learning is used to learn the initial weight and task distribution is learned with the Chinese restaurant process. It sounds like an interesting idea and practical for RL. Extensive experiments show the effectiveness of the proposed method. The authors said that online updating the meta-learner did not improve the results, which is a bit surprised. Also how many data are meta-trained is not clearly described in the paper. Maybe the authors can compare the results with less data for meta-training.