Optimal Strong Regret and Violation in Constrained MDPs via Policy Optimization
CMDP;strong regret;strong violations;primal-dual
Yes
N/A
No ethics review needed.
Since loop-free MDPs are only a slight generalization of episodic MDPs, the dependency on $H$ also matters. Is $\tilde{\mathcal{O}}(L^5)$ the optimal dependency we can expect here (where $L$ is the horizon length)?
1. The paper studies a known open problem in literature that is of theoretical interest. The idea to consider *strong* versions of regret and constraint violation is reasonable and well justified.
2. The proof is checked to be correct, and the results do advance the theoretical understanding of policy optimization in CMDPs to a certain level.
3. I like Section 5.2 that compares the proposed algorithm against known algorithms.
This paper studies efficient online policy optimization in "*loop-free*" constrained MDPs (CMDPs) that slightly generalizes finite-horizon episodic CMDPs, where by "efficient" it refers to avoiding any optimization over the space of occupancy measures. In the *bandit-feedback* setting, it proposes $\texttt{CPD-PO}$, a primal-dual policy optimization algorithm built upon $\texttt{PO-DB}$ that achieves $\tilde{\mathcal{O}}(\sqrt{T})$ *strong* regret/violation bounds.
1. This paper only deals with finite-horizon episodic MDPs with *bandit feedback*, which is a more restrictive setting than Efroni et al. (2020) and Müller et al. (2024). It seems a little unfair to directly compare against those algorithms that do not require bandit feedback.
2. The algorithmic contribution is limited since $\texttt{CPD-PO}$ largely builds upon $\texttt{PO-DB}$, only adding a simple binary dual update scheme.
3. Despite the theory-oriented approach of this paper, it is still helpful to include at least some simulation results to illustrate the applicability of the proposed algorithm.
4. The paper does not discuss about its limitations and future directions.
    * For example, the algorithm seems intractable since it requires the exact value of $\rho$, which is generally unavailable in practice. It would be much better if it can work with only an upper/lower bound of $\rho$, which does not seem to be the case here.
5. Suggestions on writing:
    * Avoid squeezing key formulations (i.e., the *loop-free* MDP setting) into the footnote, even given the page limit.
    * Clearly convey your message and ideas in the explanatory paragraphs following any mathematical results. For example, the paragraphs following Lemma 3 can be improved (What's the "aforementioned parameters"? Why does eq. (2) hold?). 
    * The constants in Lemma 1 & 2 seem inconsistent from those in Lemma 6, up to a numerical factor.
6. Minor typesetting issues:
    * There are a few typos in the paper: $K$ should be $L$ in line 5 of Algorithm 2; missing $i$ in $i \in [m]$ in line 904; etc.
    * I would personally avoid using $\verb|\nicefrak|$ or anything similar to it because it makes fractions hard to read, esp. when you have something like $A+B+C / D+E+F$.
Yes
No ethics review needed.
- What is the role of the probability distributions in line 129 in algorithm? 

- How large the margin $\rho$ is? What is the practical implication when it is infinitely small?

- Is it efficient to run the adversarial policy optimization oracle? 

- Can the authors point out the new analysis that avoids the oscillation issue in typical primal-dual methods or compare their key analysis ideas?
- It is crucial to characterize stronger regret and constraint violation in online constrained MDPs since the transitional average performance metrics may obscure policy cancellation that is not permitted in safe policy learning. 

- The authors provide an optimal regret and constraint violation bound by only considering the non-negative terms. This improves the previous suboptimal bound in the online constrained learning setting. 

- The authors propose a new primal-dual online learning algorithm, which is different from the previous work that studies the strong regret and constraint violation. Rather than using regularization, the authors introduce several changes to the standard primal-dual methods: (1) binary dual update; (2) synthetic loss for policy optimization; (3) optimize policy through an existing adversarial policy optimization oracle.
The paper studies an online learning problem in constrained MDPs. The authors propose a new constrained online learning algorithm that leverages an existing unconstrained policy optimization oracle. The authors prove that this method has the optimal regret and constrained violation bounds in a strong sense. This improves the state of the art bound of online learning in constrained MDPs.
- The authors focus on the basic tabular case of constrained MDPs. This method needs further generalization to extend beyond the tabular case.

- It would be helpful if the authors could clarify the motivation behind the techniques used in the proposed algorithm. Notably, the standard primal-dual policy optimization suffers the oscillation issues, potentially causing linear strong regret and constraint violation.  

- The proposed algorithm employs an existing adversarial policy optimization oracle to update the policy. The policy optimization oracle is designed in the adversarial setting, while the constrained MDP problem assumes stochastic rewards, costs, and fixed transitions. It would be helpful if the authors could explain the rationale behind this choice. 

- The adversarial policy optimization oracle minimizes the average type regret. It would be helpful if the extra technique to obtain a tighter regret bound can be highlighted.

- To illustrate the practical utility and verify the algorithm's performance, it would be helpful if the authors provided experimental results.
Yes
No ethics review needed.
Please see the weakness.
+ The concept of strong constraint violation is more relevant for safe-critical applications. It is also more challenging and technical than the conventional violation.

+ The paper proposed a primal-dual algorithm with an interesting dual design. The theoretical performance on regret and violation is also provided.
This paper studies online learning in constrained MDPs with strong regret and strong violation, where the negative terms are not allowed to compensate for positive ones. For this problem, this paper’s algorithm uses a primal-dual approach with UCB-like updates on the dual variables. The method achieves optimal $O(\sqrt{T})$ strong regret/violation, which improves the $O(T^{0.93})$ bound in the state-of-the-art works.
- The paper missed a few important related references (e.g., [1] and [2]), where the strong violation has been investigated and better results than this paper have been established. In [1], the OptPess-LP algorithm can satisfy the constraints instantaneously, which seems better than $O(\sqrt{T})$ strong violation.  In [2], the paper proposed a model-free method to achieve $O(\sqrt{T})$ strong violation. It would be better to discuss these papers in detail and highlight the differences. 

- The algorithm requires Slater's condition and the knowledge of Slater's constant $\rho$, which is usually not practical in most critical applications. Besides, the regret is in the order of $O(1/\rho),$ it could be problematic when $\rho$ is close to zero. 

- I understand it is a theory paper; however, including numerical experiments to validate the proposed algorithm would be beneficial. For example, the baselines could be Efroni et al. (2020), [1] and [2]. 

[1] Tao Liu, Ruida Zhou, Dileep Kalathil, PR Kumar, and Chao Tian. Learning policies with zero or bounded constraint violation for constrained MDPs. NeurIPS 2021.

[2] Arnob Ghosh, Xingyu Zhou, and Ness Shroff. Towards Achieving Sub-linear Regret and Hard Constraint Violation in
Model-free RL. AISTATS 2024.
Yes
No ethics review needed.
1. Could the authors provide more technical reasons why $\rho$ is required in this paper? Does this factor also appear in previous papers?
2. Is there any regret lower bound in this setting that is related to the number of constraints $m$?
1. The problem studied in this paper is well-motivated and the strong regret/violation metric is reasonable.
2. The author successfully improves the regret bound in this setting from $T^{0.93}$ to the optimal $\sqrt{T}$ for computationally efficient algorithms. This is a huge improvement.
3. The writings are clear and discussion about previous works are sufficient.
This paper studies learning constrained tabular MDPs with strong regret and violation guarantees. Prior works in this setting are either computationally inefficient or highly suboptimal. This work provides the first computationally efficient policy optimization algorithm with optimal $\sqrt{T}$ regret. The authors achieve this by leveraging the advance of adversarial MDPs for the primal update and an optimistic estimation for the dual update.
1. This paper's algorithm and regret bound rely on a problem-dependent factor $\rho$, which could be small and lead to worse regret.
2. This paper does not have an empirical comparison. Although this is typically not necessary for a theoretical paper, simulation results like Muller et al. [2024] could be helpful.
3. A conclusion and discussion section is lacking.
@inproceedings{
anonymous2024optimal,
title={Optimal Strong Regret and Violation in Constrained {MDP}s via Policy Optimization},
author={Anonymous},
booktitle={Submitted to The Thirteenth International Conference on Learning Representations},
year={2024},
url={https://openreview.net/forum?id=8eNLKk5by4},
note={under review}
}
We study online learning in constrained MDPs (CMDPs), focusing on the goal of attaining sublinear strong regret and strong cumulative constraint violation. Differently from their standard (weak) counterparts, these metrics do not allow negative terms to compensate positive ones, raising considerable additional challenges. Efroni et al. (2020) were the first to propose an algorithm with sublinear strong regret and strong violation, by exploiting linear programming. Thus, their algorithm is highly inefficient, leaving as an open problem achieving sublinear bounds by means of policy optimization methods, which are much more efficient in practice. Very recently, Muller et al. (2024) have partially addressed this problem by proposing a policy optimization method that allows to attain $\widetilde{\mathcal{O}}(T^{0.93})$ strong regret/violation. This still leaves open the question of whether optimal bounds are achievable by using an approach of this kind. We answer such a question affirmatively, by providing an efficient policy optimization algorithm with $\widetilde{\mathcal{O}}(\sqrt{T})$ strong regret/violation. Our algorithm implements a primal-dual scheme that employs a state-of-the-art policy optimization approach for adversarial (unconstrained) MDPs as primal algorithm, and a UCB-like update for dual variables.
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CMDP strong regret strong violations primal-dual
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Optimal Strong Regret and Violation in Constrained MDPs via Policy Optimization
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