Paper:
Imagination-Augmented Agents for Deep Reinforcement Learning
We introduce Imagination-Augmented Agents (I2As), a novel architecture for deep reinforcement learning combining model-free and model-based aspects. In contrast to most existing model-based reinforcement learning and planning methods, which prescribe how a model should be used to arrive at a policy, I2As learn to interpret predictions from a learned environment model to construct implicit plans in arbitrary ways, by using the predictions as additional context in deep policy networks. I2As show improved data efficiency, performance, and robustness to model misspecification compared to several baselines.

Review:
This paper presents an approach to model-based reinforcement learning where, instead of directly estimating the value of actions in a learned model, a neural network processes the model's predictions, combining with model-free features, to produce a policy and/or value function. The idea is that since the model is likely to be flawed, the network may be able to extract useful information from the model's predictions while ignoring unreliable information. The approach is studied in procedurally generated Sokoban puzzles and a synthetic Pac-Man-like environment and is shown to outperform purely model-free learning as well as MCTS on the learned model.
---Quality---
I feel the authors have done an exceptional job of presenting and evaluating their claims. The experiments are thorough and carefully designed to tease issues apart and to clearly answer well-stated questions about the approach. I found the experiments to provide convincing evidence that I2A is taking advantage of the learned model, is robust to model flaws, and can leverage the learned model for multiple tasks.
I had one question regarding the experiments comparing I2A and MCTS. I2A was trained for millions of steps to obtain a good policy. Was a comparable amount of data used to train the MCTS leaf evaluation function? I looked in the appendix, but didn't see that detail. Also, out of curiosity, if you have access to a perfect simulator, isn't it a bit funny to make I2A plan with the learned model? Why not skip the model learning part and train I2A to interpret rollouts from the simulator?
---Clarity---
I felt the paper was exceptionally well-written. The authors did a good job of motivating the problem and their approach and clearly explained the big ideas. As is often the case with deep learning papers, it would be *extremely* difficult to reproduce these results based purely on the paper itself -- there are always lots of details regarding architecture, meta-parameters, etc. However, the appendix does a good job of narrowing this gap. I hope the authors will consider publishing source code as well.
---Originality---
As far as I am aware, this is a highly novel approach to MBRL. I have never seen anything like it, and it is fundamentally, conceptually different than existing MBRL approaches.
The authors did a good job of citing relevant work (the references section is prodigious). Nevertheless, there has been some other recent work outside the context of deep nets studying the problem of planning with approximate, learned models that the authors may consider discussing (recognizing of course the limitations of space). For instance, Talvitie UAI 2014 and AAAI 2016, Jiang et al. AAMAS 2015, Venkatraman et al. ISER 2016. The presented approach is unquestionably distinct from these, but there may be connections or contrasts worth making. Also, the I2A architecture reminds me of the Dyna architecture, which similarly combines model-based and model-free updates into a single value function/policy (the difference being that it directly uses reward/transition predictions from the model). Again, I2A is definitely new, but the connection may be valuable to discuss.
---Significance---
I think this is an important paper. It is addressing an important problem in a creative and novel way, with demonstrably promising results. It seems highly likely that this paper will inspire follow-up work, since it effectively proposes a new category of MBRL algorithms that seems well-worth exploring.
***After author response***
I have read the authors' response and thank them for the clarifications.