Model-based imitation learning from state trajectories
Imitation learning from demonstrations usually relies on learning a policy from trajectories of optimal states and actions. However, in real life expert demonstrations, often the action information is missing and only state trajectories are available. We present a model-based imitation learning method that can learn environment-specific optimal actions only from expert state trajectories. Our proposed method starts with a model-free reinforcement learning algorithm with a heuristic reward signal to sample environment dynamics, which is then used to train the state-transition probability. Subsequently, we learn the optimal actions from expert state trajectories by supervised learning, while back-propagating the error gradients through the modeled environment dynamics. Experimental evaluations show that our proposed method successfully achieves performance similar to (state, action) trajectory-based traditional imitation learning methods even in the absence of action information, with much fewer iterations compared to conventional model-free reinforcement learning methods. We also demonstrate that our method can learn to act from only video demonstrations of expert agent for simple games and can learn to achieve desired performance in less number of iterations.
 The problem addressed here is imitation learning when no action information is available, which is an important problem in robotics for instance. The main idea of the proposed method is to produce a policy that matches the states observed in the expert trajectories, and this is achieved via a somewhat complex mix of model-free and model-based learning. My main issues with the paper are: - It does not cite or discuss a very important piece of related work: "Imitation from Observation: Learning to Imitate Behaviors from Raw Video via Context Translation" (Liu et al., 2017) - The empirical results are unconvincing - it seems like in all problems they use there is a straightforward mapping from state feature differences to actions, as pointed out in an anonymous comment. Additionally, it would have been nice to show empirically how helpful the model-based component of their approach is.
You are a member of the scientific community tasked with peer review. Review the following paper title and abstract.
### Title + Abstract
Model-based imitation learning from state trajectories
Imitation learning from demonstrations usually relies on learning a policy from trajectories of optimal states and actions. However, in real life expert demonstrations, often the action information is missing and only state trajectories are available. We present a model-based imitation learning method that can learn environment-specific optimal actions only from expert state trajectories. Our proposed method starts with a model-free reinforcement learning algorithm with a heuristic reward signal to sample environment dynamics, which is then used to train the state-transition probability. Subsequently, we learn the optimal actions from expert state trajectories by supervised learning, while back-propagating the error gradients through the modeled environment dynamics. Experimental evaluations show that our proposed method successfully achieves performance similar to (state, action) trajectory-based traditional imitation learning methods even in the absence of action information, with much fewer iterations compared to conventional model-free reinforcement learning methods. We also demonstrate that our method can learn to act from only video demonstrations of expert agent for simple games and can learn to achieve desired performance in less number of iterations.
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 problem addressed here is imitation learning when no action information is available, which is an important problem in robotics for instance. The main idea of the proposed method is to produce a policy that matches the states observed in the expert trajectories, and this is achieved via a somewhat complex mix of model-free and model-based learning. My main issues with the paper are: - It does not cite or discuss a very important piece of related work: "Imitation from Observation: Learning to Imitate Behaviors from Raw Video via Context Translation" (Liu et al., 2017) - The empirical results are unconvincing - it seems like in all problems they use there is a straightforward mapping from state feature differences to actions, as pointed out in an anonymous comment. Additionally, it would have been nice to show empirically how helpful the model-based component of their approach is.
assistant
 The problem addressed here is imitation learning when no action information is available, which is an important problem in robotics for instance. The main idea of the proposed method is to produce a policy that matches the states observed in the expert trajectories, and this is achieved via a somewhat complex mix of model-free and model-based learning. My main issues with the paper are: - It does not cite or discuss a very important piece of related work: "Imitation from Observation: Learning to Imitate Behaviors from Raw Video via Context Translation" (Liu et al., 2017) - The empirical results are unconvincing - it seems like in all problems they use there is a straightforward mapping from state feature differences to actions, as pointed out in an anonymous comment. Additionally, it would have been nice to show empirically how helpful the model-based component of their approach is.