Combining Model-based and Model-free RL via Multi-step Control Variates
Model-free deep reinforcement learning algorithms are able to successfully solve a wide range of continuous control tasks, but typically require many on-policy samples to achieve good performance. Model-based RL algorithms are sample-efficient on the other hand, while learning accurate global models of complex dynamic environments has turned out to be tricky in practice, which leads to the unsatisfactory performance of the learned policies. In this work, we combine the sample-efficiency of model-based algorithms and the accuracy of model-free algorithms. We leverage multi-step neural network based predictive models by embedding real trajectories into imaginary rollouts of the model, and use the imaginary cumulative rewards as control variates for model-free algorithms. In this way, we achieved the strengths of both sides and derived an estimator which is not only sample-efficient, but also unbiased and of very low variance. We present our evaluation on the MuJoCo and OpenAI Gym benchmarks.
 The paper studies a combination of model-based and model-free RL. The idea is to train a forward predictive model which provides multi-step estimates to facilitate model-free policy learning. Some parts of the paper lack clarity and the empirical results need improvement to support the claims (see details below). Clarity - The main idea of the proposed method is clear. - Some notations and equations are broken. For example: (1) The definition of .bar{A} in Section 4 is broken. (2) The overall objective in Section 5 is broken. (3) The computation of w in Algorithm 2 is problematic. - Some details of the experiments/methods are confusing. For example: (1) The step number k is dynamically determined by a short line search as in Section 4 ``Dynamic Rollout’’, but later in the experiments (Section 6) the value of k is set to be 2 uniformly. (2) Only the policy and value networks specified. The forward models are not specified. (3) In algorithm 1, what exact method is used in determining if .mu is converged or not? Originality The proposed method can be viewed as a multi-step version of the stochastic value gradient algorithm. An empirical comparison could be helpful but not provided. The idea of the proposed method is related to the classic Dyna methods from Sutton. A discussion on the difference would be helpful. Significance - The paper could compare against other relevant baselines that combine model-based and model-free RL methods, such as SVG (stochastic value gradient). - To make a fair comparison, the results in Table 1 should consider the amount of data used in pre-training the forward models. Current results in Table 1 only compare the amount of data in policy learning. - Figure 3 is plotted for just one random starting state. The Figure could have been more informative if it was averaged over different starting states. The same issue is found in Figure 2. It would be helpful if the plots of other domains are provided. - In Figure 2, even though the diff norm fluctuates, the cosine similarity remains almost constant. Does it suggest the cosine similarity is not effective in measuring the state similarity? - Figure 1, 4 and 5 need confidence intervals or standard errors. Pros: - The research direction in combining model-based and model-free RL is interesting. - The main idea of the proposed method is clear. Cons: - Parts of the paper are unclear and some details are missing. - The paper needs more discussion and comparison to relevant baseline methods. - The empirical results need improvement to support the paper’s claims.
You are a member of the scientific community tasked with peer review. Review the following paper title and abstract.
### Title + Abstract
Combining Model-based and Model-free RL via Multi-step Control Variates
Model-free deep reinforcement learning algorithms are able to successfully solve a wide range of continuous control tasks, but typically require many on-policy samples to achieve good performance. Model-based RL algorithms are sample-efficient on the other hand, while learning accurate global models of complex dynamic environments has turned out to be tricky in practice, which leads to the unsatisfactory performance of the learned policies. In this work, we combine the sample-efficiency of model-based algorithms and the accuracy of model-free algorithms. We leverage multi-step neural network based predictive models by embedding real trajectories into imaginary rollouts of the model, and use the imaginary cumulative rewards as control variates for model-free algorithms. In this way, we achieved the strengths of both sides and derived an estimator which is not only sample-efficient, but also unbiased and of very low variance. We present our evaluation on the MuJoCo and OpenAI Gym benchmarks.
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 studies a combination of model-based and model-free RL. The idea is to train a forward predictive model which provides multi-step estimates to facilitate model-free policy learning. Some parts of the paper lack clarity and the empirical results need improvement to support the claims (see details below). Clarity - The main idea of the proposed method is clear. - Some notations and equations are broken. For example: (1) The definition of .bar{A} in Section 4 is broken. (2) The overall objective in Section 5 is broken. (3) The computation of w in Algorithm 2 is problematic. - Some details of the experiments/methods are confusing. For example: (1) The step number k is dynamically determined by a short line search as in Section 4 ``Dynamic Rollout’’, but later in the experiments (Section 6) the value of k is set to be 2 uniformly. (2) Only the policy and value networks specified. The forward models are not specified. (3) In algorithm 1, what exact method is used in determining if .mu is converged or not? Originality The proposed method can be viewed as a multi-step version of the stochastic value gradient algorithm. An empirical comparison could be helpful but not provided. The idea of the proposed method is related to the classic Dyna methods from Sutton. A discussion on the difference would be helpful. Significance - The paper could compare against other relevant baselines that combine model-based and model-free RL methods, such as SVG (stochastic value gradient). - To make a fair comparison, the results in Table 1 should consider the amount of data used in pre-training the forward models. Current results in Table 1 only compare the amount of data in policy learning. - Figure 3 is plotted for just one random starting state. The Figure could have been more informative if it was averaged over different starting states. The same issue is found in Figure 2. It would be helpful if the plots of other domains are provided. - In Figure 2, even though the diff norm fluctuates, the cosine similarity remains almost constant. Does it suggest the cosine similarity is not effective in measuring the state similarity? - Figure 1, 4 and 5 need confidence intervals or standard errors. Pros: - The research direction in combining model-based and model-free RL is interesting. - The main idea of the proposed method is clear. Cons: - Parts of the paper are unclear and some details are missing. - The paper needs more discussion and comparison to relevant baseline methods. - The empirical results need improvement to support the paper’s claims.
assistant
 The paper studies a combination of model-based and model-free RL. The idea is to train a forward predictive model which provides multi-step estimates to facilitate model-free policy learning. Some parts of the paper lack clarity and the empirical results need improvement to support the claims (see details below). Clarity - The main idea of the proposed method is clear. - Some notations and equations are broken. For example: (1) The definition of .bar{A} in Section 4 is broken. (2) The overall objective in Section 5 is broken. (3) The computation of w in Algorithm 2 is problematic. - Some details of the experiments/methods are confusing. For example: (1) The step number k is dynamically determined by a short line search as in Section 4 ``Dynamic Rollout’’, but later in the experiments (Section 6) the value of k is set to be 2 uniformly. (2) Only the policy and value networks specified. The forward models are not specified. (3) In algorithm 1, what exact method is used in determining if .mu is converged or not? Originality The proposed method can be viewed as a multi-step version of the stochastic value gradient algorithm. An empirical comparison could be helpful but not provided. The idea of the proposed method is related to the classic Dyna methods from Sutton. A discussion on the difference would be helpful. Significance - The paper could compare against other relevant baselines that combine model-based and model-free RL methods, such as SVG (stochastic value gradient). - To make a fair comparison, the results in Table 1 should consider the amount of data used in pre-training the forward models. Current results in Table 1 only compare the amount of data in policy learning. - Figure 3 is plotted for just one random starting state. The Figure could have been more informative if it was averaged over different starting states. The same issue is found in Figure 2. It would be helpful if the plots of other domains are provided. - In Figure 2, even though the diff norm fluctuates, the cosine similarity remains almost constant. Does it suggest the cosine similarity is not effective in measuring the state similarity? - Figure 1, 4 and 5 need confidence intervals or standard errors. Pros: - The research direction in combining model-based and model-free RL is interesting. - The main idea of the proposed method is clear. Cons: - Parts of the paper are unclear and some details are missing. - The paper needs more discussion and comparison to relevant baseline methods. - The empirical results need improvement to support the paper’s claims.