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Title: Explain Your Move: Understanding Agent Actions Using Focused Feature Saliency

Paper content:
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.
and deep sequential models (Karpathy et al., 2015) .
However, interpretability for RL-based agents has received significantly less attention.
Interpreting the strategies learned by RL agents can help users better understand the problem that the agent is trained to solve.
For instance, interpreting the actions of a chess-playing agent in a position could provide useful information about aspects of the position.
Interpretation of RL agents is also an important step before deploying such models to solve real-world problems.
Inspired by the popularity and use of saliency maps to interpret in computer vision, a number of existing approaches have proposed similar methods for reinforcement learning-based agents.
derive saliency maps that explain RL agent behavior by applying a Gaussian blur to different parts of the input image.
They generate saliency maps using differences in the value function and policy vector between the original and perturbed state.
They achieve promising results on agents trained to play Atari games.
Iyer et al. (2018) compute saliency maps using a difference in the action-value (Q(s, a)) between the original and perturbed state.
There are two primary limitations to these approaches.
The first is that they highlight features whose perturbation affects actions apart from the one we are explaining.
This is illustrated in Figure 1 , which shows a chess position (it is white's turn).
Stockfish 1 plays the move Bb6 in this position, which traps the black rook (a5) and queen (c7) 2 .
The knight protects the white bishop on a4, and hence the move works.
In this position, if we consider the saliency of the white queen (square d1), then it is apparent that the queen is not involved in the tactic and hence the saliency should be low.
However, perturbing the state (by removing the queen) leads to a state with substantially different values for Q(s, a) and V (s).
Therefore, existing approaches Iyer et al., 2018) mark the queen as salient.
The second limitation is that they highlight features that are not relevant to the action to be explained.
In Figure 1c , perturbing the state by removing the black pawn on c6 alters the expected reward for actions other than the one to be explained.
Therefore, it alters the policy vector and is marked salient.
However, the pawn is not relevant to explain the move played in the position (Bb6).
In this work, we propose a perturbation based approach for generating saliency maps for black-box agents that builds on two desired properties of action-focused saliency.
The first, specificity, captures the impact of perturbation only on the Q-value of the action to be explained.
In the above example, this term downweights features such as the white queen that impact the expected reward of all actions equally.
The second, relevance, downweights irrelevant features that alter the expected rewards of actions other than the action to be explained.
It removes features such as the black pawn on c6 that increase the expected reward of other actions (in this case, Bb4).
By combining these aspects, we generate a saliency map that highlights features of the input state that are relevant for the action to be explained.
Figure 1 illustrates how the saliency map generated by our approach only highlights pieces relevant to the move, unlike existing approaches.
We use our approach to explain the actions taken by agents for board games (Chess and Go), and for Atari games (Breakout, Pong and Space Invaders).
Using a number of illustrative examples, we show that our proposed approach obtains more focused and accurate interpretations for all of these setups when compared to and Iyer et al. (2018) .
We also demonstrate that our approach is more effective in identifying important pieces in chess puzzles, and further, in aiding skilled chess players to solve chess puzzles (improves accuracy of solving them by nearly 25% and reduces the time taken by 31% over existing approaches).
We presented a perturbation-based approach that generates more focused saliency maps than existing approaches by balancing two aspects (specificity and relevance) that capture different desired characteristics of saliency.
We showed 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.
The results of our technique show that saliency can provide meaningful insights into a black-box RL agent's behavior.
For experiments on Go, we use the pre-trained MiniGo RL agent: https://github.com/ tensorflow/minigo.
This agent was trained using the AlphaGo Algorithm .
It also adds features and architecture changes from the AlphaZero Algorithm Silver et al. (2017) .
For experiments on Atari agents and for generating saliency maps for , we use their code and pre-trained RL agents available at https://github.com/greydanus/ visualize_atari.
These agents are trained using the Asynchronous Advantage Actor-Critic Algorithm (A3C) (Mnih et al., 2016) .
For generating saliency maps using Iyer et al. (2018) , we use our implementation.
All of our code and more detailed results are available in our Github repository: https://github.com/ rl-interpretation/understandingRL .
For chess and Go, we perturb the board position by removing one piece at a time.
We do not remove a piece if the resulting position is illegal.
For instance, in chess, we do not remove the king.
For Atari, we use the perturbation technique described in .
The technique perturbs the input image by adding a Gaussian blur localized around a pixel.
The blur is constructed using the Hadamard product to interpolate between the original input image and a Gaussian blur.
The saliency maps for Atari agents have been computed on the frames provided by in their code repository.
The puzzles for conducting the Chess human studies, creating the Chess Saliency Dataset, and providing illustrative examples have been taken from Lichess: https://database.lichess.
org/.
The puzzles for illustrative examples on Go have been taken from OnlineGo: https: //online-go.com/puzzles.
Figure 8 shows the saliency maps generated by our approach for the top 3 moves in a chess position.
The maps highlight the different pieces that are salient for each move.
For instance, Figure 8a shows that for the move Qd4, the pawn on g7 is important.
This is because the queen move protects the pawn.
For the saliency maps in Figures 8b and 8c , the pawn on g7 is not highlighted.

assistant: text: We propose a model-agnostic approach to explain the behaviour of black-box deep RL agents, trained to play Atari and board games, by highlighting relevant features of an input state.