The authors claim that they bridge an important gap between IBC [2] and RvS by modeling the dependencies between the state, action, and return with an implicit model on Page 6. However, noticing that IBC proposes to use the implicit model to model the dependencies between the state and action, I think the contribution of this paper is to introduce the return from RvS to the implicit model. Thus, the proposed method looks like a combination of IBC and RvS.
The authors conduct experiments in Section 5.1 to show the advantages of the implicit model. However, such advantages are similar to IBC, which could hurt the novelty of this paper. The authors may want to highlight the novelty of the proposed method against IBC.
The discussions of the empirical results in Sections 5.1 and 5.2.2 are missing. The authors may want to explain: 1) why the RvS method fails to reach either goal and converges to the purple point in Figure 4(b); 2) why the explicit methods perform better than implicit methods on the locomotion tasks.
The pseudo-code of the proposed method is missing.
[1] Søren Asmussen and Peter W Glynn. Stochastic simulation: algorithms and analysis, volume 57. Springer, 2007.
[2] P. Florence, C. Lynch, A. Zeng, O. A. Ramirez, A. Wahid, L. Downs, A. Wong, J. Lee, I. Mordatch, and J. Tompson. Implicit behavioral cloning. In Proceedings of the 5th Conference on Robot Learning. PMLR, 2022.
2) why the explicit methods perform better than implicit methods on the locomotion tasks. The pseudo-code of the proposed method is missing. [1] Søren Asmussen and Peter W Glynn. Stochastic simulation: algorithms and analysis, volume 57. Springer, 2007. [2] P. Florence, C. Lynch, A. Zeng, O. A. Ramirez, A. Wahid, L. Downs, A. Wong, J. Lee, I. Mordatch, and J. Tompson. Implicit behavioral cloning. In Proceedings of the 5th Conference on Robot Learning. PMLR, 2022.