Title: PARAMETERIZED ACTION REINFORCEMENT LEARN-ING FOR INVERTED INDEX MATCH PLAN GENERATION Anonymous

Abstract: Match plan generation in the inverted index at Microsoft Bing is used to be based on hand-crafted rules. We formulate the generation process as a Parameterized Action MDP with sharing parameters and purpose a reinforcement learning algorithm on such formulation. We combine deterministic policy learning on discrete and continuous action spaces and several recent advances in deep reinforcement learning. For exploring in the parameterized action space, the agent outputs softmax values for discrete actions and applies Parameter Space Noise on policy network to unify the exploration direction in both spaces. We apply prioritized recurrent replay on match plan sequences and pad short match plans. We also use invertible value function rescaling and n-step return to stabilize the training. The agent is evaluated on our environment and some benchmarks. It outperforms the well-designed production match plan and beats the baselines on the benchmarks.

1. INTRODUCTION:

Using machine learning to optimize and accelerate software and hardware systems is an emerging field in the past few years (Mirhoseini et al. (2017) ; Rosset et al. (2018) ). The process of decisionmaking in those systems is usually hand-crafted by human engineers, which is less explored to be automatically done by learning algorithms. A promising direction is to formulate such sequential decision-making problems as Reinforcement Learning (RL) problems, such as search plan generation problem in inverted indexes (Rosset et al. (2018) ).

The inverted index is a specialized data structure that is commonly used in search engines, including Microsoft Bing1 . An inverted index provides an inverted mapping from term to documents. Each term has a posting list which contains all the (document, location) pairs that the term appears. By combining the posting lists of the terms in a user query, the initial document candidates are generated (Witten et al. (1999) ; Zobel & Moffat (2006) ).

In Bing, documents are scanned with specified match plans, either predefined or generated in realtime. A match plan has a sequence of rewrites (match rules, e.g., it treats the query as a phrase which should appear in the document exactly as it is), where each rewrite can be controlled by several quotas (stopping criteria). There are several types of rewrites, and all these types share the same continuous quotas. A pair of a rewrite type and the quotas forms a single action which determines if a document will be a ranking candidate.

We formulate the generation problem as a RL problem. The state consists of system runtime signals and semantic embeddings of queries. The action space is called parameterized or discretecontinuous hybrid, where an action has a discrete action and continuous action-parameters. The reward is weighted by result quality and query latency. It is similar to Parameterized Action RL (Masson et al. ( 2016)) (PARL), while our setting requires all actions to share same parameters.

Previously, the match plans are predefined manually for each query. It hardly utilizes the rich information in the state to dynamically adjust the rewrites and quotas for specific query online. Rosset et al. (2018) tries to automatically generate match plans using tabular Q-learning with discretized state space and predefined action-parameters. They learn the generation policy for a specific query class each time and solve it only in discretized spaces with tabular methods. We extend the genera- Figure 1: Match plan example: for the query "Reinforcement Learning", the search engine firstly gets a document posting list for each term from the inverted index, and then scans the document candidates following a serial of rewrites (match rules) according to the match plan.

tion process to the general case, such that the match plans are fully parameterized and learned from scratch without any predefined knowledge (e.g., limited match rules).

In this paper, we purpose a parameterized action RL algorithm that learns to act on parameterized action space environments, such as the match plan generation process. We introduce normalized softmax values of discrete actions to form a categorical distributions to enable gradients backpropagation. We also combine several recent advances in RL to accelerate and stabilize the training. We investigate parameter space noise (Plappert et al. (2017) ) on parameters of the policy for unifying directions in exploring the structured action space. We explore recurrent deterministic policies (Heess et al. (2015) ) with prioritized replay buffer (Schaul et al. (2015) ) on sequence, due to the inherent nature of partial observability and sparse feedback in our setting. We also use n-step return and invertible value function rescaling (Kapturowski et al. (2018) ) for further stability.

We present an agent to integrate these techniques and evaluate on offline training on a query dataset collected from Bing search. When training on the dataset with uniformly sampling, the agent outperforms the currently deployed production match plan, which is generated based on well-designed hand-crafted rules. We study the performance of training on the dataset or only with several selected complicated queries individually, as well as ablation studies for the various components. We test on a few existing PARL benchmarks, where our agent beats our baseline methods and performs the state-of-the-art results.

2. BACKGROUND:



2.1. REINFORCEMENT LEARNING:

We address the problem using reinforcement learning (RL) framework with a parameterized action space. An agent interacts with an environment to maximize the accumulated reward with discount factor γ ∈ [0, 1). We model the environment with a discrete-time Partially Observable Markov Decision Process (POMDP). A POMDP is given by a tuple (S, A, T, R, Ω, O), and the underlying Markov Decision Process (MDP) is defined by (S, A, T, R), where S is the state space, A the parameterized action space, T the transition function T : S × S × A → R + , and R : S × A → R is the reward function. The set of observations is given by Ω and the the observation function mapping underlying states to probability distributions over observations is given by O.

Specifically, the environment in the match plan generation has a parameterized action space. In Masson et al. (2016); Bester et al. (2019) , such formulation in fully observable settings is referred as Parameterized Action MDP (PAMDP), where the action space is denoted as

A = k∈A d {(k, x k )|x k ∈ X k } ,

where each discrete action a ∈ A d = [K] has a corresponding continuous action-parameter space X a . However, our setting requires a slightly different formulation:

A = {(k, x)|k ∈ A d , x ∈ X } = A d × X

, where the discrete action space (rewrite) A d share the action-parameter space X . Such formulation results in a disentangled action space between discrete and continuous actions which a class of parameterized action (P-DQN (Xiong et al. (2018) ) and MP-DQN (Bester et al. (2019) )) may not be trivially applicable to. Considering the notational simplicity in partially observable environment (POMDP), we do not explicitly denote it when there is no ambiguity in action settings.

2.2. FORMULATION OF MATCH PLAN GENERATION:

In Rosset et al. (2018) , the action-parameters are predefined for the match plan generation, where the action space is then a set of discrete rewrites (actions). We fully parameterize a match plan to allow the agent to generate any valid plans.

State. At each time step, the agent receives a state with two parts. First part has selected run-time system signals from the inverted index system. Since the query is uniformly sampled in different episode, we include some statistical features and semantic embeddings of queries to allow the agent to identify different queries and then generate corresponding match plans. Such statistical features are used in the current production system, such as the length of a query. All signals and embeddings are normalized to a reasonable range around [-1, 1] based on empirical estimations.

Action. The agent selects a parameterized action a t including a rewrite id and its allocated quotas at each step. There are 29 types of rewrites in A d and also a 5-dimensional quota parameter space X for each rewrite in int64 type. However, the range of each quota is empirically set to valid values and then normalized to [-1, 1]. All outputted quotas are effective for the current step.

There is also a special action to note: stop (by agent). Normally, the environment will return a terminal signal, but it happens in extreme cases, such as low system resources. To allow a better balance of latency and performance, we allow the agent to choose stop or not as another type of discrete action, which is the same level as other 29 rewrites.

Reward. We use two criterions in the design: latency and performance. For latency consideration, we use a signal Seek Count that is constant for a same query in different runs with same match plan, instead of executed time. We weight the Ranking Scores of top five returned documents as an indicator of performance with weights [0.4, 0.2, 0.2, 0.1, 0.1]. When there are less than five documents returned, the scores of missing documents are treated as a minimum possible ranking score. The final scalar reward is weighted by these two objectives to balance them. We do not use the division in Rosset et al. (2018) , since the loss surfaces may become more non-convex for optimizing.

We also have punishments (negative rewards) for some special cases. One is to assign a punishment when the agent selects to illegally stop at the first step or some unsupported rewrites (for some special queries). However, unsupported rewrites do not necessarily cause a terminal state, since the production system has same hand-crafted match plans for a class of queries, in which the unsupported rewrites are simply omitted.

3. METHOD:

For generating match plans, we introduce a method that works on the aforementioned formulation with slightly different parameterized action space than standard PAMDPs in the literature. We start from the intuition with deterministic policy learning (Silver et al. (2014) ) and how it relates to parameterized actions. PAMDP often refers to the formulation that each individual discrete action k ∈ A d = [K] has a corresponding continuous action-parameter space. Thus, there are K continuous action-parameter spaces corresponding to K discrete action (Xiong et al. (2018) ). In our shared-parameter PAMDP, the Bellman equation incorporated both discrete action k and continuous action-parameters x is given by:

Q (s, k, x) = E r,s r + γ max k sup x ∈X Q (s , k , x ) |s, k, x .

(1) P-DQN (Xiong et al. (2018) ) tackles PAMDP by incorporating multiple action-parameter policies x k (s; θ x ) : S → X k for each action k and update them with

max k sup x k ∈X k Q (s , k , x k )

simultaneously. However, since all parameters x are shared for actions k in our case, we do not require multiple policies. The policy loss in P-DQN (Xiong et al. (2018) ) given by Following this intuition, we directly parameterize the policy with two heads µ : S → R K × X with θ µ as :

L x (θ x ) = E s∼D - K k=1 Q (s, k, x k (s; θ x ) ; θ Q ) (2)

k sof t t , x t = a sof t t = µ(s t ; θ µ ),

where k sof t t and a sof t t refers to action-parameters or actions with softmax values. One head outputs the sof tmax values for all discrete actions. For another head, it outputs normalized action-parameter values with hyperbolic tangent (tanh) activation after the continuous head. The environment will denormalize it to a valid range. This corresponds to the Squashing Gradients method for bounded-continuous action spaces in Hausknecht & Stone (2015) .

Exploration with Parameter Space Noise and action sampling. In DQN (Mnih et al. (2015) ), the behavioral policy is given by -greedy strategy for exploration purpose, where an agent randomly selects a discrete action with probability < 1. In DDPG (Lillicrap et al. (2015) ), although the policy learned is deterministic, to explore the action space, the behavioral policy needs to be different because of the off-policy nature. It uses action space noise such as uncorrelated Gaussian noise or correlated Ornstein-Uhlenbeck process (Lillicrap et al. (2015) ).

Directly combining such two exploration strategies is straightforward. However, it may induce a problem that the exploration in two action spaces A = A d × X in different paces. For example, the match plan may need another rewrite with larger quotas in a step. However, if the exploration strategy is to use -greedy for a rewrite k and Gaussian noise for its quotas x, the agent may require more samples to discover the potential rewards (positive documents). We instead use parameter space noise (Plappert et al. (2017) ) on parameterized action space to tackle such issue.

We denote the discrete and continuous action heads as (π sof t k (s), π x (s)) = µ(s; θ µ ). To compute the distance d(π, π) = D KL (π π) of non-perturbed and perturbed policies µ(s; θ µ ), µ(s; θ µ ), we use weighted sum of the distance of discrete and continuous actions. For the continuous actions π x (s), the distance is given by E s (π x (s) i -π x (s) i )

2 to estimate KL-divergence empirically. For the discrete actions, we use outputted softmax probabilities to compute D KL (π sof t k (s) π sof t k (s)). The state and action pairs are sampled from a replay memory. The variance σ of the noise is updated after a policy update based on the distance and threhold δ (Plappert et al. (2017) ). The policy and target networks with layer normalization (Ba et al. ( 2016)) are perturbed per episode.

Prioritized replay and recurrent policies. We use recurrent architecture to obtain the underlying system state of the POMDP for both value and policy networks (Heess et al. (2015) ). To avoid recurrent state staleness (Kapturowski et al. (2018) ), we store and replay a sequence of (s, k sof t , x, r) with softmax values into a replay memory. Since a match plan is usually short, we set a maximum length and pad shorter episodes with zero or randomly sampled states and actions.

The sampling from a replay memory can be prioritized with a probability p i proportional to TDerrors (Schaul et al. (2015) ) to increase the sample efficiency in such structured action space. To prioritize the transitions with well-matched documents, we slightly modify the sampling strategy. With a half probability, the agent uses regular prioritization, otherwise partitions the memory to multiple bins and retrieve a transition (sequence) with max reward from each bin. Our strategy provides a more efficient and balanced exploration strategy of the evaluation.

Policy update. In the update, we use clipped double Q-learning and delayed policy update (Fujimoto et al. (2018) ) to stabilizing the training. After the agent samples a batch of (sequences), it perturbs the target policy network and computes the perturbed actions for target policy smoothing. The parameter space noise variance σ is then updated with the sampled states and actions.

The policy is still deterministic since it is learned in off-policy and only the exploration involves stochasticity. The update is given by deterministic policy gradients theorem (Silver et al. (2014) ):

EQUATION

)

where D is a replay memory. We found this policy architecture is similar to PA-DDPG (Hausknecht & Stone (2015) ), while it does not including sof tmax activation for discrete actions and other advanced techniques. We also build upon other useful techniques such as invertible value function rescaling h(x) = sign(x)( |x| + 1 -1) + x (Kapturowski et al. ( 2018)):

EQUATION

)

where a * = (max k sof t t , x t ) = (k t , x t ) is the greedy action, y the target for updating Q-network, and θ - Q denotes the target Q-network. The policy network outputs differentiable softmax values and is updated by the gradients backpropagated from the Q-network:

EQUATION

where the expectation is took over samples from a memory.

The pseudocode for the algorithm with more details can be found in the Appendix.

4. EXPERIMENTS:

In this section, we apply the purposed agent on the inverted index match plan generation problem. We create a dataset which contains a set of queries and corresponding query embeddings. We perform various ablation study on how each component interacts and the benefits of them. We also test on other Paramterized Action RL benchmarking baselines.

4.1. EXPERIMENT SETTINGS:

For match plan generation, we experiment on a dataset that has about 100,000 queries sampled from Bing search log. In the current production system, each query is classified to a predefined query classes online based on a set of rules which are related to statistical features of the query. Each query class has some hand-crafted rules which outputs a match plan for the classified query. We use each query's production match plan as baseline. The match plan for each query is generated by the hand-crafted rules and does not change over each running. We only skip a few special queries that do not have embeddings or need additional operations beyond match plans.

The generated match plan is evaluated by the delta values of both Ranking Score and Seek Count and overall reward. We provide learning curves of delta values of evaluation rewards. Note that the reward are not symmetrical around 0, since the ranking scores has a minimum value and is sparse since a few queries are hard to find good documents and will be assigned a very low score. It may significantly pull down the average rewards shown in the curves, thus we present histograms for fair comparison. For the benchmarks, we report the results on evaluation reward curves during tuning.

4.2. MATCH PLAN GENERATION PERFORMANCE EVALUATION:

We first visualize the query embeddings in Figure 3 using UMAP (McInnes et al. (2018) ) to demonstrate our learned models. We evaluate 6,000 queries and draw the colormap based on the difference between evaluation reward and production baseline reward. Although most queries perform similar to the well-designed production rules, there are some clusters of queries and some patterns exist. We notice that there is a main cluster at the center (about (-7, -1)) that gathers most queries on which our agent does not perform well. That cluster contains some random inputs from the users, which may have some typos. For the queries that the agent outperforms the baseline, we find they are quite scattered. We guess the reason is that the baseline with hand-crafted rules do not consider the embeddings, thus it is less affected by embeddings. This may suggest us to look for more informative embeddings of queries to better distinguish some clustered queries.

In the plots shown in Figure 6 , we visualize the distribution of delta rewards, ranking scores and seek count values comparing to the well-designed production rules. For most of the queries the agent learns promising match plan without any prior knowledge just based on the reward signals.

There are also some hard queries to find a pattern that have poor reward (about -100) and usually lies in the main cluster and around a few more small clusters. Other than some meaningless queries around the center, they also include some non-English queries, such as French and Chinese, which are possibly clustered around their centroid and are challenging to learn the policy for all of them.

Reward design. We consider different weights to trade-off between the query latency from the input and the quality of the returned documents. The quality is evaluated by Ranking Scores of top five documents. We found that it is sparse since the good documents are hard to match. When we weight more on ranking score, the agent tends to stop the search early.

We also investigate a few punishments and try to use less of them to avoid manual design. We found the agent learns some common patterns guided by the reward and punishments. It tries to avoid "stop" at the first step because we set a huge punishment on such invalid stop. This punishment is necessary since it prevents the agent stops at first step for better value than trying more steps but get no documents matched. In other words, the agent is encouraged to explore different rewrites instead of using empty match plans. Without such punishment, the performance significantly drops since it is hard to explore good documents at first. We also punish the agent to avoid unsupported rewrites, since the production system uses designed rules for each class of the queries and simply omits a rewrite without any useful feedback. We test punishing repeated rewrites, however, it helps seek count (more efficient) but may harm ranking scores.

4.3. ABLATION STUDY:

We compare different exploration noise, including action space noise and parameter space noise.

For the action space noise, the noise on discrete and continuous actions are applied separately. We test Gaussian and OrnsteinUhlenbeck noise on continuous actions, while the discrete actions only use -greedy. To keep the comparison fair, we also test -greedy strategy on continuous actions by uniformly sampling a point with probability . The parameter noise is directly applied on policy network µ(s; θ µ ). We found the parameter space noise performs more stable than all types of action space noise. The uniform sampling on both discrete and continous action with -greedy failed for sometimes, thus we did not include it in comparison. We give the relative improvement in Table 1 compared to RNN with Gaussian Noise. The results show that parameter space noise has best performance overall.

The results in Figure 6 (a) and Table 2 show that RNN got significantly better performance than MLP with or without parameterization on our environment. It indicates that the environment is highly non-Markov. We guess one obvious possibility is that the system signals of the state space cannot include all the information, while recurrent networks try to extract latent state from the history. Note that each episode will sample one query, thus the query embedding is constant for all steps in one episode. Another possible reason is that some rewrites may not be supported by special queries or have too small change for the state signals.

We compare different parameterization methods on recurrent networks. The results are shown in Figure 4 (b) and Table 2 . We empirically found that our modified strategy using reward bin is more stable from the beginning. It is possible that the agent repeatedly replays not only good experience on matching documents, but also learns to avoid punishment we set, as we expect. In general prioritized replay, the sampling probability is just based on TD-error and may be biased to worse samples since value function may not update towards possible results.

4.4. BENCHMARKING GAMES:

We experiment on Platform-v0 and Goal-v0 from Bester et al. (2019) . Note that, in all these games, each discrete action has a separate continuous action-parameter space. Our algorithm is designed for shared action-parameters and does not utilize such prior, thus we do not compare with P-DQN-style algorithms. We apply n-step return since the episode in these games is much longer. We assume the environment is fully observable and do not use recurrent networks in comparison.

Hyperparameters. We examine the games with different combinations of hyperparameters, since they are easy to parallize on each training nodes without the need to connect to an production environment emulator. With NNI, the learning rates for value and policy network are set to log-uniform in [3×10 -4 , 3×10 -3 ] and [1×10 -4 , 1×10 

-uniform in [1 × 10 -3 , 1 × 10 -2 ].

In Platform-v0 and Goal-v0, we found the agent is not very sensitive to the range we set, such as α, δ and τ . The δ values of top 20% trials vary between [0.05, 0.3], while α and τ values are evenly scattered in the defined range. In these settings, usually the agent prefers slightly larger value learning rate than policy learning rate. 2019)) and P-DQN (Xiong et al. (2018) ). We use reported number from the papers, while last two methods report another metric on Goal-v0.

5. RELATED WORK:

While the aforementioned algorithms and techniques work on discrete or continuous action spaces, it is not trivial to apply them on parameterized action space, since such discrete-continuous hybrid action space is hard to parameterized by a single distribution. A related series of work is to combine DDPG and DQN to optimize Q-value function on parameter actions. There are two classes of methods that belong to them: PA-DDPG (Hausknecht & Stone (2015) ) based on DDPG and P-DQN (Xiong et al. (2018) ) based on DQN. They are Q-Learning-based methods which select the best action by maximizing the Q-value function on discrete action space or learning a deterministic policy outputting best continuous action. However, they use different strategies to combine discrete and continuous actions. Bester et al. (2019) (MP-DQN) extends P-DQN to tackle the problem that Q-value is a function of the joint action-parameter vector Q(s , k , x Q (s )) in normal PAMDP, which may results in fault gradients. However, such problem does not exist in our slightly modified setting, since the action-parameter space X in the match plan generation is inherently defined to be shared for each k ∈ A d . Masson et al. (2016) purposes a method to iteratively optimizing discrete and continuous actions by alternating between them. Another perspective (Klimek et al. (2017) ; Wei et al. (2018); Fu et al. (2019) ) for a parameterized action space is to regard it as a twohierarchy action space, where an agent selects discrete action first and continuous parameter later. However, we do not consider this direction in current scheme because we share the same parameters for all discrete action in not very large scale. Therefore, the hierarchical methods may not bring a significant performance gain.

6. DISCUSSIONS:

In this paper, we present a parameterized action RL match plan generation method which extends the plan generation to the general case without any predefined knowledge. Key to address the problem are normalized softmax values of discrete actions to enable gradients backpropagation, parameter space noise on parameters of the policy for unifying the exploration direction in both discrete and continuous spaces, and recurrent deterministic policies with prioritized replay buffer to accelerate and stabilize the training. Our algorithm can be applied to not only the match plan generation environment, but also other similar parameteried action environments. The experiment results demonstrate our method outperforms the well-designed hand-crafted rules in Bing and serveral baseline results in some existing PARL benchmarks. In this paper, we mainly discuss about offline training procedure. In the future, we plan to apply learned policy to the production environment.

:

https://www.bing.com

https://github.com/microsoft/nni

https://github.com/microsoft/pai

Figures and Tables:

Figure 3: Visualization of query embeddings.

Figure 4: Different techniques.

Figure 5: Delta Reward Distribution in Evaluation Phrase.

degenerates to a single policy in next case, where the gradient ∇L x (θ x ) exactly is the deterministic policy gradient(DPG, Silver et al. (2014)). MP-DQN (Bester et al. (2019)) states a problem regarding to the erroneous gradients of Q-network ∇L Q in P-DQN caused by multiple action-parameter policies backpropagating gradients at the same time. However, it does not exist in such single network situation. This is a desired architecture since separating multiple actionparameter policies loses the knowledge that they share same potential meanings.

Table content: <table><tr><td/><td/><td/><td/><td>Policy Network</td><td colspan="2">Param Noise</td><td>Env</td></tr><tr><td>Env</td><td>observation</td><td>Recurrent Head</td><td>state</td><td colspan="2">action with softmax</td><td>perturbed max action</td></tr><tr><td/><td/><td/><td/><td>Value Network</td><td>Q-value</td></tr><tr><td/><td/><td colspan="4">Figure 2: The diagram of the process.</td></tr></table>

Improved Scores of Different Noise Compared to RNN Gaussian Noise. Ranking Score and Seek Count are scaled to match Overall = RankingScore-SeekCount for easy comparison.

Table content: <table><tr><td/><td colspan="3">Overall Ranking Score (Quality) Seek Count (Efficiency)</td></tr><tr><td>OU Noise</td><td>+1.00</td><td>+1.20</td><td>-0.20</td></tr><tr><td colspan="2">Param Noise +1.69</td><td>+0.82</td><td>-0.87</td></tr><tr><td/><td/><td colspan="2">Overall Ranking Score (Quality) Seek Count (Efficiency)</td></tr><tr><td>MLP + Prioritized</td><td/><td>+14.54 +8.56</td><td>-5.98</td></tr><tr><td>RNN + Prioritized</td><td/><td>+32.63 +14.44</td><td>-18.19</td></tr><tr><td colspan="3">RNN + Prioritized Sample +32.67 +15.29</td><td>-17.38</td></tr></table>

Improved Scores of Different Model Compared to MLP

Table content: <table/>

-3 ]. The α in parameterized replay is set to log-uniform in [0.2, 1.0]. The action noise threshold δ in parameter space noise is set to log-uniform between [0.05, 0.8]. Tn soft parameter update, the τ is set to log

Table content: <table/>

Average evaluation results (the average of all training rewards and final evaluation reward) on benchmarks Platform-v0 and Goal-v0 with PA-DDPG(Hausknecht & Stone (2015)),MP-DQN  (Bester et al. (

Table content: <table><tr><td colspan="2">Average Eval Return Our</td><td>PA-DDPG</td></tr><tr><td>Platform-v0</td><td colspan="2">0.9573 0.3113</td></tr><tr><td>Goal-v0</td><td>34.20</td><td>-6.208</td></tr></table>

Peer review (iclr2020):
This work presents an approach for training policies to generate match plans using reinforcement learning. Match plans are used in search engines to retrieve documents for a given user query. One of the challenging features of this problem is that the action space is a mix of discrete and continuous parameters. This work formulates the task as an RL problem, and proposes a number of design decisions for training an effective policy. This paper presents an interesting application of RL, but overall the technical innovation of the method is fairly modest, and mainly consists of different components that are already commonly used in previous work. The writing also has a fair bit of awkward phrasing and grammatical errors that hurt the clarity of the exposition. For these reasons, I do not think this work meets the bar for ICLR in its current form. More specific notes: As someone who is not familiar with search engines, I have some trouble understanding the concept of a match plan from the description provided in the paper. I’m not sure how the rewrite process works. Perhaps adding more details of this, maybe in the appendix, can help readers better understand the task. The captions for the figures are not very informative. In Table 2, what is the difference between “prioritized” and “prioritized sample”? In Figure 3, it is not clear why it is necessary to show the embedding. Most samples seem to show similar performances and it’s not clear if there is any obvious structure. Perhaps this figure can be simply replaced with a history of performances. While Section 4.2 suggests that some clusters with worse or better performance exists, this is not really evident in the plot itself. I’m also not sure what “We guess the reason is that the baseline with hand-crafted rules do not consider the embeddings, thus it is less affected by embeddings.” means. How does the fact that the baseline does not use the embedding affect any structures in the embedding? The paper provides a fairly thorough review of a lot of different design decisions, but perhaps it is not necessary to go into such excruciating details for all of the different components. Some details such as padding short episodes with zeros are probably not vital for the overall narrative. It might be appropriate to relocate some of these details to the appendix.