First, we rewrite Eq. (3) as max

This paper is concerned with multi-view reinforcement learning (MVRL), which allows for decision making when agents share common dynamics but adhere to different observation models. We define the MVRL framework by extending partially observable Markov decision processes (POMDPs) to support more than one observation model and propose two solution methods through observation augmentation and cross-view policy transfer. We empirically evaluate our method and demonstrate its effectiveness in a variety of environments. Specifically, we show reductions in sample complexities and computational time for acquiring policies that handle multi-view environments.

Originality: Considering that multi-view and multi-modal RL papers tend to offer ad-hoc solutions to the problem, this paper's formalization is a nice contribution. Quality & Clarity: The paper is well presented, the math is mostly clear, although some parts aren't obviously translatable to an implementation. In terms of experiments, it seems that many details are lacking, and as far as I call tell, all figures represent a single run of each setting, which is worrisome. Significance: While the contributed framework does seem like a useful formalism, this paper fails to convince me that it actually is: - The proposed experiment in 4.1 creates artificial views which don't seem representative of multimodal settings, in that they all contain the same *information*. It would have been more convincing to feature an experiment where views are truly independent when conditioned on the current state (e.g.

Reviewers are all excited about the extension of model-free and model-based RL to multi-view/multi-modal settings. The authors offer formal formulation of multi-view RL by extending POMDP. The model-free case is rather straight-forward, and the model-based case leads to variational inference. Reviewers however, expressed concern regarding the setup of toy examples and the quality of the evaluation. Overall, the merit of the work outweigh the weakness.

This paper is concerned with multi-view reinforcement learning (MVRL), which allows for decision making when agents share common dynamics but adhere to different observation models. We define the MVRL framework by extending partially observable Markov decision processes (POMDPs) to support more than one observation model and propose two solution methods through observation augmentation and cross-view policy transfer. We empirically evaluate our method and demonstrate its effectiveness in a variety of environments. Specifically, we show reductions in sample complexities and computational time for acquiring policies that handle multi-view environments.

This paper is concerned with multi-view reinforcement learning (MVRL), which allows for decision making when agents share common dynamics but adhere to different observation models. We define the MVRL framework by extending partially observable Markov decision processes (POMDPs) to support more than one observation model and propose two solution methods through observation augmentation and cross-view policy transfer. We empirically evaluate our method and demonstrate its effectiveness in a variety of environments. Specifically, we show reductions in sample complexities and computational time for acquiring policies that handle multi-view environments. Papers published at the Neural Information Processing Systems Conference.