Explain Your Move: Understanding Agent Actions Using Focused Feature Saliency
As deep reinforcement learning (RL) is applied to more tasks, there is a need to visualize and understand the behavior of learned agents. Saliency maps explain agent behavior by highlighting the features of the input state that are most relevant for the agent in taking an action. Existing perturbation-based approaches to compute saliency often highlight regions of the input that are not relevant to the action taken by the agent. Our approach generates more focused saliency maps by balancing two aspects (specificity and relevance) that capture different desiderata of saliency. The first captures the impact of perturbation on the relative expected reward of the action to be explained. The second downweights irrelevant features that alter the relative expected rewards of actions other than the action to be explained. We compare our approach with existing approaches on agents trained to play board games (Chess and Go) and Atari games (Breakout, Pong and Space Invaders). We show through illustrative examples (Chess, Atari, Go), human studies (Chess), and automated evaluation methods (Chess) that our approach generates saliency maps that are more interpretable for humans than existing approaches.
Interpreting the policies of RL agents is an important consideration if we would like to actually deploy them and understand their behaviours. Prior works have applied saliency methods to DRL agents, but the authors note that there are two properties - specificity and relevance - that these methods do not take into account, and therefore result in misleading saliency maps. The authors define (and provide examples) of these properties, propose simple ways to calculate these (and like prior methods, relying on perturbations and therefore applicable to almost black box agents), and combine them neatly using the harmonic mean to provide a new way to calculate saliency maps for agents with discrete action spaces. While the improvements on Atari are hard to quantify, the results on chess and go are more interpretable and hence more convincing. The study using saliency maps to aid human chess players is rather neat, and again adds evidence towards the usefulness of this technique. Finally, the chess saliency dataset is an exciting contribution that can actually be used to quantify saliency methods for DRL agents. The proposed method is relatively simple, but is well-motivated and demonstratively (quantitatively, which is great work for general interpretability methods) better than prior methods, and in addition the authors introduce a new dataset + quantitative measure for saliency methods, so I would give this paper an accept. Although the authors motivate their choice of perturbation-based saliency methods as opposed to gradient-based methods, they should expand their review of the latter. As the technique the authors introduced is very general, it would be useful to know how it compares to the current state of research in terms of identifying properties that attribution methods should meet - a good example of this is integrated gradients (Sundararajan et al., 2017), which similarly identify *sensitivity* and *implementation invariance* as *axioms* that their method satisfies.
You are a member of the scientific community tasked with peer review. Review the following paper title and abstract.
### Title + Abstract
Explain Your Move: Understanding Agent Actions Using Focused Feature Saliency
As deep reinforcement learning (RL) is applied to more tasks, there is a need to visualize and understand the behavior of learned agents. Saliency maps explain agent behavior by highlighting the features of the input state that are most relevant for the agent in taking an action. Existing perturbation-based approaches to compute saliency often highlight regions of the input that are not relevant to the action taken by the agent. Our approach generates more focused saliency maps by balancing two aspects (specificity and relevance) that capture different desiderata of saliency. The first captures the impact of perturbation on the relative expected reward of the action to be explained. The second downweights irrelevant features that alter the relative expected rewards of actions other than the action to be explained. We compare our approach with existing approaches on agents trained to play board games (Chess and Go) and Atari games (Breakout, Pong and Space Invaders). We show through illustrative examples (Chess, Atari, Go), human studies (Chess), and automated evaluation methods (Chess) that our approach generates saliency maps that are more interpretable for humans than existing approaches.
user
<think>Okay, to do a decent peer review, I should start with summarizing what the work is about. This includes novelty, contributions, clarity, and impact. I might include suggestions to issues that I find critical to the usefulness of the work. Starting with novelty, there are different types of novelty. The first aspect of novelty is scope and scale. Is the work covering a broader perspective than previous works? The second aspect is methodology. Is the proposed methodology novel compared to previous methods? Do they provide any significant advantage over previous methodologies? The third aspect is results. Are the results different compared previous work? Does this lead to any novel theories or an extension of current theories? Next is clarity, I will be reading the work in depth and analyze if any sentences can be potentially ambiguous or misleading. If any sentences are ambiguous, I will state them and give a suggestion on how to rephrase such sentence or how to make more clear to the audience. Lastly, impact is important. I will attempt to predict how such work will impact the scientific community. This might include the applications of such work and how the work might transform the way we study a certain topic. Finally, I will might only mention issues that are critical. This is to keep the review as concise and helpful as possible.</think>



Interpreting the policies of RL agents is an important consideration if we would like to actually deploy them and understand their behaviours. Prior works have applied saliency methods to DRL agents, but the authors note that there are two properties - specificity and relevance - that these methods do not take into account, and therefore result in misleading saliency maps. The authors define (and provide examples) of these properties, propose simple ways to calculate these (and like prior methods, relying on perturbations and therefore applicable to almost black box agents), and combine them neatly using the harmonic mean to provide a new way to calculate saliency maps for agents with discrete action spaces. While the improvements on Atari are hard to quantify, the results on chess and go are more interpretable and hence more convincing. The study using saliency maps to aid human chess players is rather neat, and again adds evidence towards the usefulness of this technique. Finally, the chess saliency dataset is an exciting contribution that can actually be used to quantify saliency methods for DRL agents. The proposed method is relatively simple, but is well-motivated and demonstratively (quantitatively, which is great work for general interpretability methods) better than prior methods, and in addition the authors introduce a new dataset + quantitative measure for saliency methods, so I would give this paper an accept. Although the authors motivate their choice of perturbation-based saliency methods as opposed to gradient-based methods, they should expand their review of the latter. As the technique the authors introduced is very general, it would be useful to know how it compares to the current state of research in terms of identifying properties that attribution methods should meet - a good example of this is integrated gradients (Sundararajan et al., 2017), which similarly identify *sensitivity* and *implementation invariance* as *axioms* that their method satisfies.
assistant
Interpreting the policies of RL agents is an important consideration if we would like to actually deploy them and understand their behaviours. Prior works have applied saliency methods to DRL agents, but the authors note that there are two properties - specificity and relevance - that these methods do not take into account, and therefore result in misleading saliency maps. The authors define (and provide examples) of these properties, propose simple ways to calculate these (and like prior methods, relying on perturbations and therefore applicable to almost black box agents), and combine them neatly using the harmonic mean to provide a new way to calculate saliency maps for agents with discrete action spaces. While the improvements on Atari are hard to quantify, the results on chess and go are more interpretable and hence more convincing. The study using saliency maps to aid human chess players is rather neat, and again adds evidence towards the usefulness of this technique. Finally, the chess saliency dataset is an exciting contribution that can actually be used to quantify saliency methods for DRL agents. The proposed method is relatively simple, but is well-motivated and demonstratively (quantitatively, which is great work for general interpretability methods) better than prior methods, and in addition the authors introduce a new dataset + quantitative measure for saliency methods, so I would give this paper an accept. Although the authors motivate their choice of perturbation-based saliency methods as opposed to gradient-based methods, they should expand their review of the latter. As the technique the authors introduced is very general, it would be useful to know how it compares to the current state of research in terms of identifying properties that attribution methods should meet - a good example of this is integrated gradients (Sundararajan et al., 2017), which similarly identify *sensitivity* and *implementation invariance* as *axioms* that their method satisfies.