Title: Towards Safe and Generalizable Treatment Strategies in Healthcare via RL and PAC-Bayesian Computations
Venue: NeurIPS 2025
Accepted: no
Keywords: Reinforcement Learning

PAC-Bayes

Healthcare
Abstract: Reinforcement learning (RL) offers a promising paradigm for optimizing treatment strategies that adapt over time to patient responses. However, the deployment of RL in clinical settings is hindered by the lack of generalization guarantees, an especially critical concern given the high-stakes nature of this domain. Existing generalization bounds for sequence data are either vacuous or rely on relaxations of the independence condition, which often produce non-sharp bounds and limit their applicability to RL. In this work, we derive a novel PAC-Bayesian generalization bound for RL that explicitly accounts for temporal dependencies arising from Markovian data. Our key technical contribution integrates a bounded-differences condition on the negative empirical return to establish the applicability of a McDiarmid-style concentration inequality tailored to dependent sequences such as Markov Decision Processes. This leads to a PAC-Bayes bound with explicit dependence on the Markov chain’s mixing time. We show that our bound can be directly applied to off-policy RL algorithms in continuous control settings, such as Soft Actor-Critic. Empirically, we demonstrate that our bound yields meaningful confidence certificates for treatment policies in simulated healthcare environments, providing high-probability guarantees on policy performance. Our framework equips practitioners with a tool to assess whether an RL-based intervention meets predefined safety thresholds. Furthermore, by closing the gap between learning theory and clinical applicability, this work advances the development of reliable RL systems for sensitive domains such as personalized healthcare.


Peer reviews:
{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis work introduces a PAC-Bayesian framework for RL, which induces a novel soft actor critic based algorithm with guided exploration. Experiments are conducted to validate the theoretical results and the effectiveness of the proposed algorithm.\n\nSTRENGTHS AND WEAKNESSES:\nThe insight of operating the PAC-Bayes value-error bound in Theorem 3.2 for exploration and exploitation is interesting. However, the current manuscript has several questions to be addressed to justify its significance and contribution. Please see details below.\n\nQUESTIONS:\n1. The title kind of deviates the main content of this work. Some key words, like 'safe', 'generalizable' are not well defined and discussed in the main context. What is the formal definition of 'generalization', and how your methodology enhances the generalization ability? 'Safety' only appears several times in the manuscript without specific meaning. How does your method facilitate safety? How does it compare with other safe RL algorithms? For 'healthcare', it seems that the author only use application in healthcare as a motivating example, actually the method is designed for broader applications. Limited experiments (only sepsis) are conducted to evaluate the validity of the framework in healthcare. In sum, I suggest the authors considering revising the title, and provide formal and detailed discussion on 'safe' and 'generalization'. \n\n2. As for the writing, too much space are allocated to preliminaries and background information from page 2 to page 5. I think we can go into the methodology part more earlier. Section 2 can be concised to leave space for more discussion and a formal presentation of the algorithm.\n\n3. There is a line of work using sequential decision making for infectious disease control: 'Optimal treatment allocations in space and time for online control of an emerging infectious disease' and 'Deep spatial q-learning for infectious disease control'. Can you include them in discussion?\n\n4. In line 43-44, 'Most standard generalization bounds and concentration inequalities rely on the assumption that samples are independent and identically distributed (i.i.d.)', to the best of my knowledge, concentrations for martingale are commonly used in theoretical RL works, such as 'Distributionally robust off-dynamics reinforcement learning: Provable efficiency with linear function approximation', 'Minimax optimal and computationally efficient algorithms for distributionally robust offline reinforcement learning'. I think the discussion can be revised here to focus on what is the theoretical contribution of this paper.\n\n\n5. This paragraph is related to the Q1, in line 195-196, 'This PAC-Bayesian formalism allows us to reason about the generalization properties of randomized policies drawn from $\\rho$'. Can you elaborate on this?\n\n6. For theorem 3.2, equation (8),  how is the left hand side related to the prior $\\mu$? \n\n7. In line 267, 'we fit a posterior distribution $\\rho$', can you elaborate why and how the $\\rho$ is obtained like this, instead of from the lower bound in (9)?\n\n8. For the experiment, current results do not show advantage over existing baselines. One could question the significance of the proposed method. Moreover, safety and generalization properties are not fully validated by the limited experiments. Seeds are not reported in the manuscript.\n\n\nMinor issues:\n\n1. equation (3), $n$ should be $m$;\n\n2. equation (5), $=$ in the definition;\n\n3. equation (6), I guess the event in the indicator should be $\\neq$\n\n4. Line 278, $H> R^4/(1-\\gamma)^4$, H should be T?\n\nLIMITATIONS:\nYes.", "TITLE": "Official Review", "RECOMMENDATION": 3, "REVIEWER_CONFIDENCE": 2, "CLARITY": 2, "IMPACT": 2, "ORIGINALITY": 2, "DATE": "2025-06-23"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nproposes a PAC-bayesian bound on estimating the value of a policy in RL. By establishing a principled bound, this work can enable the deployment of RL in sensitive settings scuh as healthcare. to show that their bounds are non-vacuous in practice, the authors develop off-policy RL algorithms based on their bound and test in a sepsis simulator and some mujoco environments.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths:\n- tackles in important problem. guarantees bounds can help accelerate deployment of RL in clinical settings\n- bound has improved constants relative to earlier work on PAC-Bayes bounds in RL\n- the authors don't just prove a bound but also derive and algorithm and test it in simulations\n\nWeaknesses\n- novelty seems limited. once you assume mixing, you're almost in IID setup. After T_mix samples, you basically get an almost independent sample. so the claims that they had to extend existing PAC Bayes theory to dependent RL setting are a little overstated. IID to mixing extensions are consider fairly straightforward.\n- knowledge of T_mix is required. In experiments, the authors claim that they estimated T_mix from autocorrelations. This is not very convincing. Outside of simulations, I doubt whether T_mix can be reliably estimated from data.\n- experiments are of doubtful clinical relevance beyond the sepsis simulator\n\nQUESTIONS:\nThe extension from IID to mixing is usually straightforward in learning theory and RL theory. What makes it challenging, if at all, in your setup?\n\nEstimating T_mix seems like a hard problem. How will this be done in practice?\n\nDeployed policies in real systems might have very high mixing time. What can be done in the real world? Why do you think your method will work outside of simulations, ie in the real world?\n\nLIMITATIONS:\nThe simulation to reality gap is most likely substantial here", "TITLE": "Official Review", "RECOMMENDATION": 3, "REVIEWER_CONFIDENCE": 3, "CLARITY": 3, "IMPACT": 2, "ORIGINALITY": 2, "DATE": "2025-06-27"}

comments: SUMMARY:
This paper proposes a novel PAC-Bayesian generalization bound for reinforcement learning (RL) in healthcare, addressing the critical challenge of temporal dependencies in Markovian data. The authors integrate bounded-differences conditions with McDiarmid-style concentration inequalities to derive a bound that explicitly depends on the Markov chain’s mixing time. They validate the framework through PB-SAC, an algorithm combining the bound with Soft Actor-Critic, demonstrating its effectiveness in simulated healthcare environments and continuous-control benchmarks. The work bridges theoretical RL guarantees with clinical applicability, providing high-probability confidence certificates for treatment policies.

STRENGTHS AND WEAKNESSES:
### Strengths
1. The paper proposes a novel PAC-Bayesian generalization bound combined with the mixing time of Markov chains to solve the problem of sequence data dependence in RL.
2. The effectiveness of proposed algorithm PB-SAC were verified in medical simulation and continuous control tasks.

### Weaknesses
1. The theory assumes the Markov chain has a finite mixing time, but real clinical data might not always meet this. The paper doesn't test how the bound performs when mixing time estimates are wrong or in non-stationary environments.
2. The authors note that using KL divergence between posteriors and priors can cause problems in high dimensions. It will be interesting to discuss alternative approaches.
3. The presentation of the experimental results is not very clear. Especially Figure 2(c) is confusing.

QUESTIONS:
1. Does there exist typos in Eq.(6)?

See Weaknesses part.

LIMITATIONS:
yes
TITLE: Official Review
CLARITY: 3
IMPACT: 3
ORIGINALITY: 3
DATE: 2025-07-03

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper proposes a novel PAC-Bayesian generalization bound for reinforcement learning that explicitly accounts for the temporal dependencies in Markov Decision Processes—addressing a key limitation of existing generalization results, which are often too loose or assume independence. The authors introduce a bounded-differences condition on negative empirical return and apply a McDiarmid-style concentration inequality adapted to Markovian data, yielding a bound with explicit dependence on the mixing time of the underlying Markov chain. They demonstrate its applicability to off-policy RL algorithms like Soft Actor-Critic and show that it provides meaningful confidence guarantees in simulated healthcare settings. Overall, the work offers a theoretically grounded and practical framework for deploying safe and reliable RL-based treatment policies in high-stakes domains like personalized medicine.\n\nSTRENGTHS AND WEAKNESSES:\nStrength:\nThe application of the PAC-Bayesian framework to reinforcement learning is novel and insightful. The background on PAC learning is well-introduced and helps contextualize the contribution.\n\nWeakness:\nWhile the paper proposes a new framework for safe and generalizable treatment strategies in healthcare using PAC-Bayesian reinforcement learning, the motivation could be more compelling. The comparison between traditional RL and PAC-Bayes RL, as well as between standard PAC analysis and PAC-Bayes, is not sufficiently discussed. Additionally, the theoretical results remain high-level and lack concrete guidance or actionable insights for real-world healthcare applications.\n\nQUESTIONS:\n1. The title emphasizes healthcare, and the paper notes that a key challenge in building trust in RL for healthcare applications is the sequential and dependent nature of RL data. However, such data dependencies are common in many real-world domains and not unique to healthcare. Could the authors elaborate on what makes healthcare particularly special in this context? Providing a more detailed framework or concrete examples from healthcare might better justify the title and clarify the domain-specific significance.\n\n2. The theoretical development and explanation of PAC-Bayesian RL remain quite abstract and high-level. It’s difficult to see how this theory directly connects to practical applications in healthcare. Could the authors provide specific, grounded examples that illustrate the advantages of using PAC-Bayes RL in healthcare settings, rather than relying solely on general PAC learning bounds? This would help clarify both the motivation and the practical value of the proposed framework.\n\nLIMITATIONS:\nyes", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 4, "CLARITY": 3, "IMPACT": 2, "ORIGINALITY": 3, "DATE": "2025-07-03"}