A Dual-Agent Adversarial Framework for Generalizable Reinforcement Learning

What strategies could be employed to reduce the computational overhead introduced by the dual-agent setup, especially in more complex or resource-constrained environments?

That's a great question! We have also attempted to reduce computational overhead, but at this time, we are unable to provide a better solution. We are very eager to explore potential ways to reduce computational costs in the future. Thank you again for your suggestion.

What impact would the framework have in environments with continuous action spaces or higher-dimensional state representations, where irrelevant features may be harder to isolate?

Since the Procgen environment uses purely visual inputs, we believe that high-dimensional state representations have already been proven effective. Additionally, we acknowledge the current lack of further exploration into continuous action spaces. We would be delighted to explore these possibilities in future work!

However, despite this, our theoretical results are not affected by changes in the state space or action space, as all our theoretical results rely only on mild assumptions.

How would performance vary in scenarios where adversarial training results in catastrophic forgetting of useful features, and what mechanisms could prevent this?

Are there any considerations for applying this approach to tasks with dynamic or evolving feature relevance, where the set of relevant features may change over time?

Thank you for your question! However, the Procgen environment is static. Our study is limited to this static environment rather than environments where agents need to continuously learn, which would require avoiding the forgetting of useful features.

References

[1] K Cobbe et al. Leveraging procedural generation to benchmark reinforcement learning.

[2] K Cobbe et al. Quantifying generalization in reinforcement learning.

[3] R Raileanu et al. Decoupling value and policy for generalization in reinforcement learning.

[4] R Raileanu et al. Automatic data augmentation for generalization in deep reinforcement learning.

[5] A Jesson et al. Improving Generalization on the ProcGen Benchmark with Simple Architectural Changes and Scale.

Dear Reviewer Pk9A,

Thank you for your strong support of our work. We will now address your concerns.

While the framework performs well on the Procgen benchmark, its applicability to real-world tasks remains untested, leaving questions about how well it generalizes outside controlled environments.

Could the method be extended or adapted to improve generalization in real-world tasks beyond the Procgen benchmark, and what modifications would be necessary to achieve this?

Thank you for your thoughtful suggestion. However, Procgen is one of the most widely used benchmarks for testing the generalization ability of agents. Typically, testing generalization performance on Procgen is sufficient to reflect the quality of an algorithm. We have noted that many high-quality published works have also used Procgen as their primary testing benchmark [1, 2, 3, 4, 5].

The dual-agent adversarial framework introduces additional computational complexity, which may pose challenges in terms of scalability and efficiency for resource-constrained systems.

We acknowledge that our framework does introduce additional computational complexity. However, in terms of the performance improvements achieved, the added computational complexity is acceptable. Additionally, we would like to emphasize the theoretical contributions of this work.

Although the framework is shown to improve generalization, more detailed ablation studies could have been included to clarify the contribution of individual components, such as the specific impact of the adversarial training mechanism.

Thank you very much for your insightful suggestion. Our framework appears to be plug-and-play, so we would like to gently remind you that, apart from combining our algorithm with the baseline PPO algorithm, there do not seem to be additional ablation experiments. The experimental results also indicate that our framework significantly improves the performance of the baseline algorithm (Figures 5 and 6).

The paper assumes that irrelevant features can be identified and suppressed, but it does not sufficiently address how to detect these features in environments where their classification is unclear or context-dependent.

How does the adversarial training framework handle environments where the distinction between relevant and irrelevant features is not well-defined or context-dependent?

Thank you for your suggestion. However, these irrelevant features are learned spontaneously by the agent and are not influenced by factors such as the contextual environment.

The comparison with state-of-the-art methods is somewhat limited, with a stronger focus on performance gains rather than in-depth analysis of differences in behavior between approaches.

We do not entirely agree with your point. We indeed emphasize performance improvement (through combining our method with the baseline algorithm) rather than comparing our performance against SOTA algorithms. Additionally, we have thoroughly analyzed why our method can enhance generalization performance (Sections 3 and 4). We also provide very detailed theoretical results (Section 3), which is one of the contributions that cannot be overlooked.

Dear Reviewer GnKk,

Thank you for your constructive feedback. We will address your concerns as follows:

The theoretical claims of the submission follow almost immediately from previous work, and do not bring any additional new knowledge.

Thank you for your feedback. However, we respectfully disagree with your viewpoint. Firstly, our theoretical results are primarily derived from the mild assumption 3.1, which we have not seen in previous works. Secondly, we derived the generalization performance lower bound (Theorem 3.2) and the training performance lower bound (Theorem 3.5) based on assumption 3.1. Although the derivation of Theorem 3.5 is partly based on previous results, we believe this conclusion is non-trivial, revealing some profound aspects of reinforcement learning generalization. Specifically, it shows that enhancing the agent's robustness to irrelevant features can improve generalization, which aligns well with our intuition.

ProcGen seems to have 16 tasks. The submission tests their proposed algorithm in only 8 of them.

Thank you for your suggestion. We presented experimental results for 8 environments because these are the environments we ran. We understand the reviewer's concerns; however, the Procgen benchmark demands significant computational resources (each algorithm requires 50M interactions with the environment and must be run with 3 different random seeds independently, which is much larger than Atari and MuJoCo). Despite this, our framework still achieved substantial improvements in generalization performance (as shown in Figure 5 for bigfish, bossfight, chaser, coinrun, and fruitbot). Additionally, we referenced several accepted high-quality conference papers that also use Procgen as a benchmark, and we found that [1] used only 4 environments, [2] used only 3 environments, and [3] used 7 environments. This suggests that the number of environments should not be a strict criterion, especially considering our significant performance improvements.

More recent techniques report substantially higher scores in the ProcGen environment.

Thank you for your reminder. We want to emphasize that: our dual-agent method serves as a plug-and-play framework which can be applied to various of RL algorithms, ranther than a newly proposed RL algorithm. Hence, we do not emphasize a direct performance comparison between our method and other state-of-the-art algorithms. Instead, we highlight the performance improvement when combining our method with PPO, demonstrating that our proposed framework indeed contributes to generalization. However, further investigating the similarities and differences between these methods and ours would be very interesting.

Another thing I want to mention is that by employing the proposed adversarial learning framework the number of encoder parameters that needs to be trained is in fact doubled. This brings a new set of questions. Is it really a fair comparison to lower capacity models as previous ones? Would the prior methods perform also well if we simply just increased the parameters in the encoder?

This is indeed a good question. Choosing an encoder with double the number of parameters for comparison is challenging, although we believe that simply doubling the parameters of the PPO encoder would not match the performance of our method. Therefore, we also presented the training curve of DAAC for reference. While you pointed out that IDDAC seems to perform better, we found that DAAC and IDDAC have similar performance, with DAAC sometimes performing better in certain environments but is much simpler to implement, making it a natural choice. Nevertheless, we apologize for the lack of a broader comparison with other baselines.

References

[1] D Ghosh et al. Why generalization in rl is difficult: Epistemic pomdps and implicit partial observability.

[2] M Laskin et al. Reinforcement learning with augmented data.

[3] Z Jia et al. Improving policy optimization with generalist-specialist learning.

[4] R Raileanu et al. Decoupling value and policy for generalization in reinforcement learning.

Dear Reviewer sf2U,

We greatly appreciate your high praise for our work!

Limited Environments and Baselines: Testing is somewhat limited in environments and baseline comparisons. Adding diverse environments, such as DMC-GB[1], and competitive baselines like PIE-G[2], SVEA[3], and ARPO[4] would provide a more complete comparison and further demonstrate the model's capabilities.

Thank you for your suggestions. In this work, we have developed a theoretical framework to guide the generalization of reinforcement learning. However, due to the high computational demands of Procgen, it is challenging to add more baseline algorithms in a short amount of time, and we apologize for this.

We want to emphasize that: our dual-agent method serves as a plug-and-play framework which can be applied to various of RL algorithms, ranther than a newly proposed RL algorithm. Hence, we present the significant performance improvement achieved when combining PPO with our framework, rather than comparing performance with other SOTA algorithms. It is worth noting that the main baseline in [4] is also PPO, and we have additionally included the more classical DAAC algorithm [5] as a reference baseline.

References

[1] N Hansen et al. Generalization in reinforcement learning by soft data augmentation.

[2] Z Yuan et al. Pre-trained image encoder for generalizable visual reinforcement learning.

[3] N Hansen et al. Stabilizing deep q-learning with convnets and vision transformers under data augmentation.

[4] MM Rahman et al. Adversarial Style Transfer for Robust Policy Optimization in Deep Reinforcement Learning.

[5] R Raileanu et al. Decoupling value and policy for generalization in reinforcement learning.

Are the two characteristics discussed in Section 4.2 sufficient to ensure robust generalization performance? If not, what additional considerations could be relevant?

We believe that these two conditions are necessary for the agent to achieve good generalization performance. There may be other conditions as well, but at a minimum, these two must be satisfied.

Are any weights needed in Equation 19? If not, why?

Thanks for your question. In the current version, no weights are required in (19), as the two KL divergences are of the same order of magnitude.

Would it be beneficial to consider heterogeneous encoders in the proposed method?

Thank you for your insightful suggestion. Within the current framework, homogeneous encoders are not strictly necessary, but since the data from both agents need to be simultaneously input to each other's encoders, using homogeneous encoders appears to be a natural choice. We would be happy to explore this further in future work.

References

[1] K Cobbe et al. Leveraging procedural generation to benchmark reinforcement learning.

[2] K Cobbe et al. Quantifying generalization in reinforcement learning.

[3] B Grooten et al. MaDi: Learning to Mask Distractions for Generalization in Visual Deep Reinforcement Learning.

[4] C Jia et al. Policy Rehearsing: Training Generalizable Policies for Reinforcement Learning.

Dear Reviewer g3LS,

Thank you very much for your support of our work. We will address your concerns below.

Currently, it seems that even without generalization, the proposed method is showing good performance. Thus it is unclear if it is due to better convergence or better generalization capability. We should be careful about this when drawing the conclusion that the proposed method has better generalization performance.

This is a good question! However, there is no need to worry, as we have already proven Theorem 3.2 (which only relies on the mild assumptions of 3.1): $\zeta(\pi)\geq\eta(\pi)-\frac{2r_{\max}}{1-\gamma}\cdot(1-M)$ which means that when optimizing the training performance $\eta(\pi)$, the lower bound of the generalization performance $\zeta(\pi)$ is also optimized. $M$ represents the integral of the training set $\mathcal{M}_{train}$ over the entire $\mathcal{M}$. Moreover, as $M$ increases, the lower bound on generalization performance is gradually optimized. Figure 2 of paper [1] provides strong evidence for this theoretical result of ours.

Second, the approach is evaluated on only one generalization setup. However, it is unclear how challenging such a generalization setting is. It would be beneficial to conduct further analysis, assessing the degree to which the proposed algorithm enhances generalization across varying levels of difficulty, such as easy, moderate, and challenging generalization cases.

Thank you for your valuable suggestion. However, we would like to clarify that the difficulty of the Procgen environments is typically set to either "easy" or "hard" [1, 2], with the "easy" environments being almost non-challenging, where simply using PPO can effectively train the agent [1]. Therefore, we chose the "hard" difficulty to introduce more challenge, and we observed significant improvements, particularly in the bigfish, chaser, and fruitbot environments as shown in Figure 5.

Additionally, the authors may want to compare and discuss their method relative to other approaches aimed at enhancing RL generalization, such as [3, 4].

We appreciate the two excellent papers you provided! Given the limited rebuttal period, a detailed comparison of their methods with ours is challenging. However, we would be happy to incorporate them into our future work.

Consider improving clarity by first defining the distribution $p_{\mathcal{M}}$ explicitly and then introducing $m$. Furthermore, the limitations and future works should be discussed in the paper.

Thank you for your incredibly thorough review. We introduce $p_{\mathcal{M}}$ first in line 132. Additionally, this paper does not specifically discuss the limitations, as we believe that the main Theorems 3.2 and 3.5 depend only on the mild Assumption 3.1. We are excited to include a more detailed analysis of our method along with others (such as [3] and [4]) in future work.

Will the theorem hold if $\mathcal{M}_{train}$ is unbounded? In RL, policies typically interact with the environment on the fly, allowing for the gathering of infinite samples.

Our theorem still holds whether $p_{\mathcal{M}}$ is bounded or unbounded. If it is bounded, the probability distribution is discrete; otherwise, it is continuous. This does not affect the main conclusions of the paper.

Dear Reviewer tBzc,

Thank you very much for your support of our work! We will now address your concerns.

Several works such as DRAC and RARL consider a multi-agent/adversarial optimization process in RL. Would be good to include an extensive evaluation of these approaches as baselines and contextualize the novelty of your approach with respect to each baseline.

Thank you for your suggestion. We want to emphasize that: our dual-agent method serves as a plug-and-play framework which can be applied to various of RL algorithms, ranther than a newly proposed RL algorithm. Hence, we did not include these additional baselines. However, further investigating the similarities and differences between these methods and ours would be very interesting.

The method is primarily evaluated in the ProcGen environment and could benefit from additional empirical evaluation with a larger set of RL benchmarks to further evaluate the efficacy of the approach.

Thank you very much for your suggestion. We note that the Procgen environments are already one of the most widely used benchmarks for testing generalization performance [1, 2, 3, 4, 5], so we primarily focused on testing within Procgen. We greatly appreciate your thoughtful comments.

GANs and other adversarial optimization techniques commonly have issues with mode collapse, vanishing gradients and convergence issues, which all make optimization more difficult. Though this is controlled with the parameter $\alpha$, would be good to consider the tradeoff of the robustness to adversarial threats and the performance of the agent.

Could you share results of the sensitivity of $\alpha$ and the selection criterion for it?

Thanks for your comments. We must admit that the current default value of $\alpha$ is set empirically to 1. Due to the high computational demands of Procgen, it may be difficult to obtain this analysis result in a short amount of time.

One claim is that the method can be widely used with a variety of algorithms. Would you be able to share results on how this approach transfers with different Online RL algorithms in the ProcGen benchmark?

Thank you for your valuable question. We understand your concerns. We chose PPO as the baseline because it is one of the most widely used algorithms in reinforcement learning, such as in RLHF, and humanoid robots typically use PPO as the default algorithm. Another consideration is that the PPO algorithm outputs a probability distribution over the action space, which aligns with the KL divergence calculation in (19). Although it is challenging to provide results combining other baselines with our method in a short amount of time, we would like to emphasize the theoretical contributions of this paper.

Would you be able to provide some qualitative analysis of the representations learned by your framework compared with those of PPO and DACC to further validate the claim that robust representations are being learned?

We understand your concerns, but we believe that good generalization performance can demonstrate this. You can observe a significant improvement in generalization performance from Figure 5, which is direct evidence that the agent is learning more robust representations.

References

[1] K Cobbe et al. Leveraging procedural generation to benchmark reinforcement learning.

[2] K Cobbe et al. Quantifying generalization in reinforcement learning.

[3] R Raileanu et al. Decoupling value and policy for generalization in reinforcement learning.

[4] R Raileanu et al. Automatic data augmentation for generalization in deep reinforcement learning.

[5] A Jesson et al. Improving Generalization on the ProcGen Benchmark with Simple Architectural Changes and Scale.