Interactive Fiction games are text-based simulations in which an agent interacts with the world purely through natural language. They are ideal environments for studying how to extend reinforcement learning agents to meet the challenges of natural language understanding, partial observability, and action generation in combinatorially-large text-based action spaces. We present KG-A2C, an agent that builds a dynamic knowledge graph while exploring and generates actions using a template-based action space. We contend that the dual uses of the knowledge graph to reason about game state and to constrain natural language generation are the keys to scalable exploration of combinatorially large natural language actions. Results across a wide variety of IF games show that KG-A2C outperforms current IF agents despite the exponential increase in action space size.
This paper proposes a knowledge graph advantage actor critic (KG-A2C) model to allow an agent to do reinforcement learning in the interactive fiction game. Under the general framework of A2C, the core contribution of the paper is to apply a graph attention network on the knowledge graph to help learn better representation of the game state and reduce the action space. Experiments on Zork1 game environment are done to verify the effectiveness of the proposed method.
Overall, this paper presents a novel contribution to reinforcement learning with augmented memory/world-state. However, I do have a few concerns regarding the baselines and other details. Given these clarifications and or comparisons in an author response, I would be willing to increase the score.
Pros:
1, I like the idea of constructing the knowledge graph as the agent roll out. I think it is a better way to construct a structural representation of the world rather than assuming the agent gonna learn everything via single hidden state vector. It also permits more explainable policy in the future. Authors do make good progress along this line.
2, The paper is well written and the design of the proposed new model seems technically reasonable.
Cons & Questions:
1, The main concern I had is regarding to the baseline. I think it is more convincing to have a baseline which leverages the same entity extraction and template-action space. In particular, it should have the same model architecture except that it uses maybe a LSTM to decode the action rather than a GAT applied on knowledge graph. Note that the baseline I am referring to is different from the LSTM-A2C baseline reported in the paper as: (1) with entity extraction, although you may not get a graph mask, but you can still have a object-mask which also reduces the action space; (2) it is not clear to me that LSTM-A2C uses the same template-action mechanism as the KG-A2C, e.g., the valid action construction procedure described in section 4.1. Without such a baseline, it is hard to fully judge how helpful the knowledge graph is.
2, How do you test the generalization of the proposed models? In particular, do you use different maps during training and testing? If the model is merely trained on one map as shown in figure 5, it may just memorizes it in the knowledge graph and overfit to this map. 
3, The details of the interaction fiction problem setup are sparse. It would be very helpful to explain what exactly the observations are in the example of Figure 2. For example, what are the game description, game feedback are in this case? 
4, In the caption of figure 1, “Solid lines represent gradient flow” is misleading. If I understood correctly, solid lines refer to the computation flow which has gradient back-propagated in the backward pass.
5, Could you explain why KG-A2C converge slower than DRRN in figure 3?
6, Do you think having a fully differentiable mechanism of building knowledge graph would help or not? Why?
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Most of my concerns are addressed by the authors' response. I increased my score.