Paper:
Deep reinforcement learning has succeeded in sophisticated games such as Atari, Go, etc. Real-world decision making, however, often requires reasoning with partial information extracted from complex visual observations. This paper presents Discriminative Particle Filter Reinforcement Learning (DPFRL), a new reinforcement learning framework for complex partial observations. DPFRL encodes a differentiable particle filter with learned transition and observation models in a neural network, which enables reasoning with partial observations over multiple time steps. While a standard particle filter relies on a generative observation model, DPFRL learns a discriminatively parameterized model trained specifically for decision making. We show that the discriminative parameterization results in significantly improved performance, especially for tasks with complex visual observations, as it circumvents the difficulty of modeling complex observations that are irrelevant to decision making. Experiments show that DPFRL outperforms state-of-the-art POMDP RL models in Flickering Atari Games, an existing POMDP RL benchmark, and in Natural Flickering Atari Games, a new, more challenging POMDP RL benchmark introduced in this work. Further, DPFRL performs well for visual navigation with real-world data in the Habitat environment.

Review:
Update: my concerns have been addressed and I have updated the score to 8
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This paper introduces 3 neat ideas for training deep reinforcement learning (DRL) agents with state variables so that they can handle partially observed environments:
1) model the latent state variable as a belief distribution, using a collection of weighted hidden states, like in the particle filter (PF), with an explicit belief update of each particle, calculation of the weight using an observation function, and a differentiable re-weighting function to get the new belief distribution,
2) base the policy on the whole set of particles, by quantifying that set using its mean as well as a collection of K learnable moments (specifically, K Moment Generating Functions, each one corresponding to a dot product between the moment variable and the hidden state of the particle),
3) instead of generating the observations, take again the idea from PF which is to measure the agreement between the current observation o_t and the i-th particle state variable h_t^i, via a learnable discriminative function.
From what I understand, the only gradients in the model come from the usual 3 RL losses, and the observation functions in the discriminative PF are trained because they weigh the particles.
The model, trained using Advantage Actor Critic (A2C) works well on the (contrived, more on that later) "flickering Natural" Atari RL environment as well as on the Habitat navigation challenge, outperforming both the GRU-based deep RL agent and the Deep Variational RL based agent that uses variational sequence auto-encoders (and extra gradients from the observation function...). The ablation analysis confirms the advantages of the 3 ideas introduced in the paper.
The paper is a very well written and the experiments are very well executed. I believe that the idea is novel. I gave this paper only a weak accept because of unclear explanation and of several missed opportunities:
* The observation function f_{obs}(h_t^i, o_t) is insufficiently explained. I understood it was trained using discriminative training. Does it mean that different observations o_t are used, and if so, how many? Or is the observation o_t the current observation of the agent, but only the h_t^i change? In which case, what makes it discriminative? Isn't there a posterior collapse, with all particles ending up bearing the same state? Does the function f_{obs} input o_t or u(o_t), where u is the convolutional network?
* These questions could be easily answered with pseudocode in the appendix.
* In section 3.1, what is the relationship between p_t(i) and f_{obs}(h_t^i, o_t)?
* Particle filters in navigation enable to store the history of the observations of the mobile robot, accumulating the laser range scans and matching them to the observations. At the end, one can visualise the map stored in a given particle, as well as visualise the point cloud of the particle coordinates and show the trajectories of these particles. Here the particles contain the hidden states of the agent. Could you similarly to traditional PF, visualise the position of the agent by matching the point cloud {{h_t^i}_i}_t to a set of observations o_k taken from the whole environment, and plotting a 2D map of weights coming from function f_{obs}(h_t, o_k) evaluated over all k?
* In the discussion, can you comment on the relationship between Monte-Carlo Tree Search in RL agents (sampling different trajectories) vs. here (sampling different states)?
* While I understand the need to use that environment for the sake of comparison to DVRL, the Atari + flickering + natural images dataset is very artificial and contrived. I would be interested in seeing more analysis of the discriminative PF RL algorithm on navigation tasks, given that that's what PF were designed for.
Some missing references:
* Early references on DRL for navigation:
Zhu et al (2016) "Target-driven visual navigation in indoor scenes using deep reinforcement learning"
Lample & Chaplot (2016) "Playing FPS games with deep reinforcement learning"
Mirowski et al (2016) "Learning to navigate in complex environments"