Venue: ICLR 2022 Workshop OSC

Reviews:

final_score: rating: 2: Good workshop paper, accept
review_content: The paper is well-written and the main approach could be understood.

Pros:
* The approach of separating out a policy intro a graph-based classification problem and then rule-mining based approach seems novel.
* The experiment results are promising

Cons: 
* Discussion regarding how such an approach could translate to standard RL benchmarks could have been discussed.
* Discussing how the approach could extend to non-text-based domains could also have been discussed.
review_title: Review

final_score: rating: 2: Good workshop paper, accept
review_content: **Summary of contribution**
This work presents a two-step policy that combines neural-network-based RL policy and a rule-based policy to deal with the knowledge graph input and state-dependent text action space. The proposed policy has the added benefits of interpretability, generalization, and robustness. Experiments on several text-based games demonstrated that the proposed approach achieves a new state-of-the-art performance for both generalization and robustness.

**Pros**
* The paper reads well
* The proposed way of combining the RL policy with rule-based policy and rule mining process is interesting
* It presents a strong empirical results


**Cons**
* The proposed method may not be readily scalable to other domain due to several assumptions (see “comment” section below for details)

**Comments**
* The proposed method seems to assume expert knowledge for constructing the “template” of action.
* The proposed rule miner’s performance relies on the quality of demonstration, hence may not be readily scalable to other domains where the demonstration does not provide enough coverage of environment “knowledge” or is not given. It would be more interesting to discuss how the proposed method can be extended to more general tasks.
* It would be interesting to discuss the relation between rule mining and precondition inference [1,2] and how they are used for policy.

[1] Bradley Hayes and Brian Scassellati. Autonomously constructing hierarchical task networks for planning and human-robot collaboration. In 2016 IEEE International Conference on Robotics and Automation (ICRA), pp. 5469–5476. IEEE, 2016.

[2] Sohn, Sungryull, et al. "Meta Reinforcement Learning with Autonomous Inference of Subtask Dependencies." International Conference on Learning Representations. 2019.

review_title: Official review

Meta-review: The reviewers agree the paper should be accepted at the workshop. Congratulations!

The authors are encouraged to include a discussion of the points raised by the reviewers when preparing the camera-ready version of the paper.

Decision: Accept (Poster)