Diversity Is Not All You Need: Training A Robust Cooperative Agent Needs Specialist Partners
We show that partners' specialization is crucial for training a robust generalist and propose a method that reduces overfitness of XP-min partners that already have good specialization
摘要
评审与讨论
The paper argues that while diversity among training partners is essential for developing robust generalist cooperative agents, specialization also plays a crucial role. The authors introduce a method for quantifying both diversity and specialization using mutual information. They highlight the limitations of the cross-play minimization (XP-min) technique, which generates diverse but overfitted partners. To address this issue, the authors propose reinforcement learning and supervised learning methods to extract beneficial behaviors while reducing overfitting. Empirical results demonstrate that these methods lead to more robust generalist agents.
优点
- The paper introduces a novel method to quantify partner diversity and specialization using mutual information.
- It effectively identifies the issue of overfitting in partners generated by the XP-min technique.
- The proposed methods are empirically validated, showing improvement in training robust generalist agents.
- The paper provides a thorough analysis of how diversity and specialization impact the robustness of generalist agents.
缺点
- The experiments are conducted within a specific cooperative environment (multi-recipe Overcooked), which may limit the generalizability of the results.
- The overfitness measurement relies on an oracle generalist, which may not always be available or practical in real-world scenarios.
问题
- Can you elaborate on the computational requirements for implementing SpecTRL and SpecTRL DAgger in different environments?
- What strategies can be employed to identify or create an oracle generalist in environments where one is not readily available?
- I noticed that the hyperparameter selection process is detailed in the appendix. However, I am still concerned: is the algorithm sensitive to hyperparameters, and can minor changes in hyperparameters cause significant performance degradation?
局限性
The authors discuss the limitations in detail in the appendix. The discussion is reasonable, and it is impractical to completely address them in the current version of the paper.
We thank the reviewer for the overall positive sentiment towards the paper and their thoughtful feedbacks.
Here, we address the questions raised by the reviewer
Can you elaborate on the computational requirements for implementing SpecTRL and SpecTRL DAgger in different environments?
The computation cost of the distillation process of SpecTRL and SpecTRL DAgger is similar to that of self-play. One can approximate the training time of SpecTRL by referencing the training time of self-play in the same environment. Although the computation cost scales linearly with the number of the source partners, one could parallelize the distillation process (e.g., one distillation pair per CPU core). For reference, in the multi-recipe Overcooked, distilling 8 partners in parallel takes 12 hours.
What strategies can be employed to identify or create an oracle generalist in environments where one is not readily available?
We believe that an efficient way to generate oracles is via reward shaping alongside usual self-play training, which we also employed in this work. This approach allows us to exert domain knowledge for the design of the oracles without requiring us to program the oracles’ behaviors.
I noticed that the hyperparameter selection process is detailed in the appendix. However, I am still concerned: is the algorithm sensitive to hyperparameters, and can minor changes in hyperparameters cause significant performance degradation?
Our proposed distillation method only introduces one additional hyperparameter, , for the SpecTRL DAgger variant. We do agree that this hyperparameter could cause performance degradation if is too big. We suggest that one should start using SpecTRL with the DAgger component (i.e. ). Then, if the performance improvement is unsatisfactory, start introducing the DAgger component with a small coefficient (e.g., 0.01) and incrementally increase the coefficient value (e.g., 0.1 or 0.2).
We have also included weaknesses mentioned by the reviewer in the general response. We hope that our responses will resolve the questions and concerns raised by the reviewer. We are happy to further discuss and clarify if the reviewer feel their comments are not addressed.
The author addressed my concerns and I improved my score.
The submission positions itself within the problem of ad-hoc teamwork: learning to cooperate with teammates previously unseen during training. Indeed, an important aspect of ad-hoc teamwork is the rule of "no prior coordination". Previous work focused on developing a rich enough set of training partners to allow good test performance. Various diversity metrics were used, such as cross-play minimization. However, these approaches lead to self-sabotaging behaviours in training partners, where they develop secret handshakes (i.e. initial sequences of actions) that identify partner types, and if the other partner fails the handshake protocol, the agent can refuse to cooperate and sabotage. The authors correctly identify that methods that resolve this handshake problem actually lead to a loss of diversity in the training set. Thus a big question asked in the paper is: how to have meaningful diversity in the training set without having the handshake problem.
They propose to measure the diversity of a population as the entropy of a function of the induced trajectory distribution under the population. The choice of function allows one to decide what kind of diversity the designer cares about. They propose to measure the overfitness (a.k.a. handshake problem) by measuring if the members of the population can in fact cooperate with a generalist oracle, an oracle agent that is assumed to not have learned handshakes. Finally the specialization is measured by the negative entropy of a specific policy. They empirically demonstrate that overfitness and/or under-specialization are both bad for training.
Their proposed solution for training set generation is simple: take a population generated by cross-play minimization, and distill it to a more specialized population by making the population members cooperate with each other more efficiently, which is achieved via reinforcement learning within population. They empirically show that this reduces the overfitness of the population generated by cross-play minimization, and leads to better training partners, without creating the loss of specialization problem compared to previous work.
优点
Originality
- The proposed method for generating training partners for ad-hoc teamwork is novel.
- The loss of specialization, diversity, and overfitness is partially novel. Especially, perhaps the diversity metric is not novel per se as similar metrics were proposed for behavioural diversity, but the authors don't claim novelty here either. The loss of specialization definition feels like folk knowledge, yet I also have not seen this defined in this way before.
- Overall: the paper is sufficiently novel, and the proposed algorithm is original.
Quality
- The experimental evaluation of the proposed method is relatively extensive, the results are presented nicely, and the overall quality of the work appears high.
Clarity
- The presentation is clear, although a bit dense. See weaknesses.
Significance
- The paper has notable significance for cooperative AI and ad-hoc teamwork.
缺点
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Personally, I am not a big fan of the Overcooked for studying ad-hoc teamwork. I believe it is too constrained. It would be interesting to see how this method performs/compares in a more open-ended task with more degrees of freedom. It might actually be easier to achieve diversity then, but harder to achieve specialisation.
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Table 2 with all the abbreviations and colours look extremely busy. I believe in some parts of the paper the amount of coloured text and abbreviations make it actually harder to parse things rather than easier. This is probably also a personal constraint on my side.
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Not a big deal per se but the related works section is incredibly sparse. It is literally 16 lines. I understand that the authors wanted to squeeze in a lot of content to the paper, but I would advise you to extend this a little bit. Perhaps not in the way of adding more citations but in the way of explaining the landscape a bit more. Although, you could also argue with this and say the stuff that is super relevant are already discussed in detail in the introduction, and left-overs are quickly explained in the Related Works section.
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The limitations and also a potential discussion on the future work is left to the appendix. Now I do not like this. Instead of a Conclusion section regurgitating the paper I just read already, I would rather see a discussion on what kind of future work this opens up, and what are the limitations, in the main paper.
问题
At this point, I do not have any questions.
局限性
Limitations discussion was left entirely to the appendix, which I do not like. However, in principle, they are discussed.
We thank the reviewer for the overall positive sentiment towards the paper and their thoughtful feedbacks.
We have included weaknesses mentioned by the reviewer in the general response. We hope that our responses will resolve the questions and concerns raised by the reviewer. We are happy to further discuss and clarify if the reviewer feel their comments are not addressed.
My comments are addressed, thank you for your response. I maintain my score.
This work studies partner diversity in the context of training a generalist agent. The authors observe that XP-min approaches, while capable of producing behavioral diversity in its teammates, generates “handshaking`` behaviors—a kind of overfitting. While MP-reg aims to correct for this overfitting, the authors hypothesize this generates a “loss of specialization” (LOS) problem. The authors empirically support this hypothesis in a controlled experiment, concluding “unspecialized or overfit partners are not good training partners”. This study leverages mathematically grounded definitinos of diversity, specialization, and overfitness (each of which requires domain knowledge and/or a trained specialist agent). The authors propose “SpecTRL” and “SpecTRL DAgger”, both of which operate on a pre-trained XP-min (and/or mutual information (MI)) based population, but aim to “reduce overfitness while maintaining the diversity and specialization” which already exists in the population. The main idea of SpecTRL is for the distilled partners to learn to cooperate with the specialization of the XP-min agents, but not their sabotaging behaviors. SpecTRL DAgger introduces supervision, which cain aid the distilled agent in learning to utilize complex handshakes that may not be discovered through random exploration. The experimental results demonstrate that MP-reg / MI increase diversity, but lose specialization—in line with the LOS hypothesis. On the other hand, the SpecTRL-based approaches successfully reduces overfitting while preserving the specialization in the XP-min population. Notably, while SpecTRL’s distillation phase reduces overfitting, repeated distillation does not further reduce it.
优点
- Work appears to be novel and grounded in relevant literature.
- The paper is well-written and very well-formatted; easy to read.
- The results are significant, and will likely be of use to the MARL community.
缺点
- The limitations section is currently in the Appendix, but as this works’ analyses contains notable limitations, the authors may consider putting this in the main body.
- The results are only validated on the Overcooked environment. While the results on this environment are thorough and promising, it is difficult to be confident in the generality of the findings without validation on more domains.’
- Table 2 is a bit hard to read; it’s nice for seeing the raw data, but it’s a lot of numbers to try to make sense of. The visual aspects of the Table are nice, but plots might have made these easier to interpret (and the raw data could have been placed in the Appendix).
问题
- I think I have some intuitive understanding for why distillation instead of regularization during training may help prevent the overfitting issue while maintaining specialization, but could the authors elaborate on this aspect? A key component of the method appears to be this distillation component, but from what I see there’s not so much discussion about the motivation/intuition/inspiration for this.
局限性
The authors appear to adequately address all major limitations in Appendix G:
- The proposed measures are just one proposition—there may be other sets that better quantify the quality of training populations.
- Most notably, domain knowledge is necessary for the behavior characteristic function (f), and for training the oracle specialists.
- Evaluated on single domain (expressed as weakness above).
We thank the reviewer for the overall positive sentiment towards the paper and their thoughtful feedbacks.
Here, we address the question raised by the reviewer
I think I have some intuitive understanding for why distillation instead of regularization during training may help prevent the overfitting issue while maintaining specialization, but could the authors elaborate on this aspect? A key component of the method appears to be this distillation component, but from what I see there’s not so much discussion about the motivation/intuition/inspiration for this.
Our aim for the distilled population is for them to learn diverse and specialized behaviors of a source XP-min population while removing sabotaging behavior (i.e., overfitness). The reason we think reward maximization (RL) objective would achieve this is that the distilled partners are incentivized to nudge the source partners to perform cooperative behaviors (which gives high return) and away from their sabotaging behaviors (which gives low return). Therefore, the distilled partners would not learn the sabotaging behaviors. Additionally, when the source partners cooperate, they do so in specialized ways as they have already learned specialized behaviors with XP-min. This means that the distilled partners will also learn these specialized behaviors as well. We’ll include more of this intuition in section 5 in the camera ready version.
We have also included weaknesses mentioned by the reviewer in the general response. We hope that our responses will resolve the questions and concerns raised by the reviewer. We are happy to further discuss and clarify if the reviewer feel their comments are not addressed.
I thank the reviewers for addressing the weaknesses, and responding to my question, which has helped me better understand that aspect of the work. I maintain my positive opinion of the work, as well as my current score.
General response to all reviewers
We thank all the reviewers for the overall positive sentiment towards the paper and their thoughtful feedbacks. Here, we address common concerns among the reviewers:
- Only evaluating with multi-recipe Overcooked (reviewer sQwt, Vvdc, and G3wa)
We agree with the reviewers that evaluation in different domains would be beneficial. While we focus on the multi-recipe Overcooked environment due to the scarcity of sufficiently complex cooperative settings designed for ad-hoc teamwork, future research could explore the generalizability of our findings to other domains as such environments become available since the algorithm is domain agnostic and should be readily applied to new domains. We believe that, given the current research landscape, multi-recipe Overcooked—a complex coordination task that covers various coordination challenges—provides a robust and suitable benchmark for this investigation.
- Table 2 is hard to read (reviewer sQwt, Vvdc)
We will improve legibility of the table in the camera-ready version.
- Limitations and discussions are not in the main text and limited related work (reviewer sQwt, Vvdc)
We’ll include the limitations and discussions sections (which are now located in the appendices) in the additional page given for the camera-ready version. We’ll also provide an extended related work section.
We hope that our responses will resolve the questions and concerns raised by the reviewers. We are happy to further discuss and clarify if any reviewers feel their comments are not addressed.
The paper considers the ad-hoc teamwork problem and proposes a method that distills the diversity and specialization of the partner population generated by the cross-play minimization technique. The proposed method is also empirically better than mixed-play regularization methods.
All reviews are positive and acknowledge this paper's contribution to the field of ad hoc teamwork, though all reviewers are concerned that the experiments were conducted in only one environment (multi-recipe Overcooked).
The authors should incorporate the promised modifications into the final version,