Reinforcement Learning with Euclidean Data Augmentation for State-Based Continuous Control

Summary

This paper introduces a novel data augmentation method for reinforcement learning in continuous control tasks. The key innovation is leveraging Euclidean symmetries inherent in these tasks by applying rotational transformations to the original states. The authors propose using a limb-based state representation instead of the standard joint-based one to make states more amenable to these transformations. Integrated with the DDPG algorithm, this method is evaluated on 10 tasks from the DeepMind Control Suite, demonstrating improvements in data efficiency and performance, especially for complex 3D tasks. The approach outperforms existing data augmentation techniques and proves computationally efficient compared to alternative methods.

Strengths

Originality: - The idea of leveraging Euclidean symmetries for data augmentation in state-based RL is novel and underexplored. - The limb-based state representation is an innovative solution enabling effective Euclidean augmentation. Quality: - Comprehensive experiments on 10 continuous control tasks, including 2D and 3D variants. - Thorough ablation studies on the effect of augmentation ratio. - Comparison against relevant baselines including standard RL algorithms and previous augmentation methods. Clarity: - The paper is well-structured. - Limitations are discussed openly. Significance: - The method shows substantial improvements on challenging 3D tasks like Humanoid_run where standard methods struggle.

Weaknesses

1. The paper doesn't clearly address how the method handles environments where Euclidean data augmentation might violate constraints (e.g., obstacles in AntMaze from MuJoCo). 2. The data composition process is not fully explained. Section 4.3 doesn't specify if $B_\text{aug}$ transitions are added to $B$ or substitute points in $B$. 3. The study is limited to 0-100% augmentation, while exploring larger multipliers could be insightful as one transition can be rotated with different angles 4. The focus is primarily on rotation transformations. Exploring other transformations like translation and reflection could provide a more comprehensive analysis. 5. In 5 out of 9 tasks, $ρ_\text{aug}$ = 0% achieves the best performance, suggesting the limb-based representation may be more critical than the data augmentation itself. However, the choice of limb-based representation seems motivated primarily by enabling Euclidean DA, which may limit generalizability. In addition, the advantages of limb-based over joint-based representations are not thoroughly justified.

Questions

1. There appears to be a typo in line 271 - it should likely read "transform states $s_t$ and $s_{t+1}$". 2. The description of rotations in Section 4.3 is confusing. Why are there two separate rotation steps mentioned? 3. Some recent state-based data augmentation methods are not well cited in the related work, including: - Hindsight experience replay (Andrychowicz et al., 2017) - Counterfactual data augmentation (Pitis et al., 2020) - MoCoDA (Pitis et al., 2022) - Guided Data Augmentation (GuDA) (Corrado et al., 2024) Particularly, GuDA, while focusing on offline-RL and including human guidance, describes a method for state-based data augmentation, including rotation, transformation, and reflection. Discussing these works could strengthen the paper's positioning in the field. 4. The paper focuses solely on the DDPG algorithm. Exploring compatibility with other popular RL algorithms would increase impact.

Rating

5

Confidence

5

Soundness

2

Presentation

3

Contribution

2

Limitations

The authors adequately address limitations, noting the need for task-specific tuning and knowledge of strict Euclidean symmetries. They provide constructive suggestions for future work. Regarding societal impact, while the immediate concerns are limited for simulated tasks, a brief discussion on long-term implications for robotics, especially on real robots, could be valuable.

Summary

This paper introduces a novel data augmentation strategy tailored for reinforcement learning (RL) agents operating in state-based continuous control environments. The method leverages limb-based state features rather than joint-based configurations, allowing for more effective augmentation. Experiments conducted on various tasks from the DeepMind Control Suite demonstrate significant improvements in data efficiency and performance over standard RL algorithms and existing augmentation methods.

Strengths

The paper introduces a unique approach to data augmentation for RL in state-based continuous control, moving away from traditional perturbation methods to Euclidean transformations. This approach is innovative and addresses the limitations of existing methods.

Weaknesses

Firstly, I am not an expert in this research field. I think this paper lacks some deeper theoretical support. The scenario covered by Theorem 1 is overly simplistic. The proposed method involves additional computations for applying Euclidean transformations and managing the limb-based state features. The paper could benefit from a more detailed discussion on the computational overhead and potential optimizations.

Questions

Could the authors provide more details on how to tune the various parameters involved in the proposed method, such as the choice of Euclidean transformations and the proportion of augmented data?

Rating

5

Confidence

2

Soundness

3

Presentation

2

Contribution

2

Limitations

The authors acknowledge the need for task-specific tuning of the hyperparameter and the requirement for knowledge of strict Euclidean symmetries. They have adequately addressed the limitations.

Summary

This paper proposes a data augmentation technique that leverages Euclidean symmetries (e.g. rotational symmetry) in a task's dynamics to generate augmented data. When a task's state features do not have such symmetries, the paper also discusses how to define a new state representation with these symmetries (so that data augmentation can be applied). Empirically, the paper shows that RL is more data efficient with this data augmentation technique than without it on various DMControl tasks.

Strengths

1. The paper focuses on a class of augmentations that have been under-studied (compared to visual augmentations) 2. Redefining a task's state representation to enable data augmentation is an interesting concept to me. 3. Comparing the proposed augmentation strategy with an RL agent that learns with an equivariant network architecture was useful; I've always wondered how these two different approaches compared.

Weaknesses

1. **The story seems improperly situated in the data augmentation literature.** While it is true that most prior data augmentation works focus on perturbation-based augmentations, many prior works have exploited task symmetries and invariances to generate additional data that agrees with the task's dynamics and reward function [1-6]. Corrado et. al [1] calls these dynamics-invariant augmentations. 2. **It’s unclear if this change of representation is necessary for data augmentation.** Corrado et. al [1-2] perform data augmentation using SO(3) rotations in MuJoCo tasks similar to those studied in this paper *without* changing the task’s state features. 3. **Weak empirical results.** In 5/9 tasks, DDPG + data augmentation performs just as well as DDPG without data augmentation. With 5 seeds per curve, I have low confidence in the observed benefits in the remaining tasks; RL is notoriously high variance, and the variance between runs is enough to create statistically different distributions. 95% confidence belts assume the distributions of returns at each evaluation point are normally distributed, which is likely not the case. [1] Corrado & Hanna. "Understanding when Dynamics-Invariant Data Augmentations Benefit Model-free Reinforcement Learning Updates." ICLR 2024. [2] Corrado et. al. "Guided Data Augmentation for Offline Reinforcement Learning and Imitation Learning." arXiv:2310.18247 [3] Pitis et. al. “Counterfactual Data Augmentation using Locally Factored Dynamics.” NeurIPS 2020. [4] Pavlov et. al. "Run, Skeleton, Run: Skeletal Model in a Physics-Based Simulation." AAAI 2018. [5] Abdolhosseini et. al. “On Learning Symmetric Locomotion.” ACM SIGGRAPH 2019. [6] Adrychowicz et. al. "Hindsight Experience Replay." NeurIPS 2017. [7] Henderson et. al. "Deep Reinforcement Learning that Matters." AAAI 2018 **Minor Comments** 1. I think section 3.2 can be omitted; the augmentation framework described in section 4.3 can be applied to any off-policy RL algorithm. 6. Line 271: typo, I think the second $s_t$ should be $s_t\prime$

Questions

1. Line 40-43: Could the authors elaborate on what “uncorrelated” means here? 4. Line 159: Does anything change if the morphology tree contains a cycle? 5. Line 272: What does it mean to keep $a_t$ and $r_t$ invariant?

Rating

4

Confidence

4

Soundness

2

Presentation

2

Contribution

1

Limitations

The paper addresses limitations, though I think an additional limitation should be emphasized: not only may itbee difficult to specify a symmetry, symmetries might not even exist in some tasks. I don't believe this limitation takes anything away from the paper though; it's a core limitation of most non-perturbation-based data augmentation methods, and it's fine if these methods only apply to some tasks. That's simply the nature of data augmentation. Thankfully, in many real-world tasks (especially robotics tasks where you have an agent acting in 3D space) a human *can* identify symmetries. Pitis et. al [1] and Corrado et. al [2] discuss this point too. [1] Pitis et. al. “Counterfactual Data Augmentation using Locally Factored Dynamics.” NeurIPS 2020. [2] Corrado & Hanna. "Understanding when Dynamics-Invariant Data Augmentations Benefit Model-free Reinforcement Learning Updates." ICLR 2024.

Summary

This work proposes a novel data augmentation approach that leverages Euclidean symmetry for continuous control to improve the efficiency and performance of RL algorithms. The authors integrated their approach with DDPG and performed a series of comparison against the vanilla SAC algorithm, other augmentation techniques, and other equivariant methods. The results show some improvements in performance across a range of continuous control tasks, especially those with rich 3D motions and large number of limbs and joints.

Strengths

1. The proposed approach improved the learning efficiency of RL algorithms (demonstrated with DDPG in this work) without the need of changes to the algorithm, which lowers the barrier for adaptability for the community. 2. Euclidean transformations maintain the inherent physics of the task, ensuring that the augmented data are still representative of realistic scenarios. This preservation is crucial for the relevance and usefulness of the augmented data.

Weaknesses

1. This method is primarily applicable to tasks with clear Euclidean symmetries. So, it seems that in environments where such symmetries are not evident or relevant, the approach may not be applicable or effective. While the 'Limitation' section briefly touches upon this issue, it is not clear what categories of continuous control problems this approach will be applicable to. 2. The effectiveness of Euclidean transformations may vary depending on whether the task is set in a 2D or 3D space, with potentially limited benefits in more constrained settings (like planar movements). 3. As the 'Limitation' section suggests, practical application of the framework will be difficult due to the task-specific hyperparameter tuning requirements. While the author suggests adaptive hyperparameter tuning as a potential future work, some insights into what characteristics of the environment dynamics dictate the optimal choice of hyperparameters such as \rho_{aug} would be helpful. Also, in the context of practical application, switching from joint-based to limb-based configurations complicate the state representation and requires significant changes to the training environment.

Questions

1. In Section 5.1, the authors say "To summarize, our method reliably improves DDPG and SAC, the state-of-the-art RL algorithms on the continuous control tasks." However, it looks like only vanilla SAC is used in this work for comparison. Hence, the mention of improving SAC should be removed. 2. In line 194 p^y_1 should be p^z_1.

Rating

5

Confidence

4

Soundness

3

Presentation

3

Contribution

3

Limitations

Technical limitations are highlighted in the paper.

Thank you

Thank you for the response. Since I am not an expert in this field, I will maintain my current neutral score. Thank you again or your detailed responses to my review.

Response to rebuttal

Thanks for the responses to my questions. I am sticking to my score.