Title: Counteractive RL: Rethinking Core Principles for Efficient and Scalable Deep Reinforcement Learning
Venue: NeurIPS 2025
Accepted: yes
Keywords: reinforcement learning

sample efficiency

scalable Deep RL

data efficiency

exploration

sample efficient reinforcement learning

temporal difference learning
Abstract: Following the pivotal success of learning strategies to win at tasks, solely by interacting with an environment without any supervision, agents have gained the ability to make sequential decisions in complex MDPs. Yet, reinforcement learning policies face exponentially growing state spaces in high dimensional MDPs resulting in a dichotomy between computational complexity and policy success. In our paper we focus on the agent’s interaction with the environment in a high-dimensional MDP during the learning phase and we introduce a theoretically-founded novel paradigm based on experiences obtained through counteractive actions. Our analysis and method provide a theoretical basis for efficient, effective, scalable and accelerated learning, and further comes with zero additional computational complexity while leading to significant acceleration in training. We conduct extensive experiments in the Arcade Learning Environment with high-dimensional state representation MDPs. The experimental results further verify our theoretical analysis, and our method achieves significant performance increase with substantial sample-efficiency in high-dimensional environments.


Peer reviews:
{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper proposes a novel method called CoAct TD Learning. While conventional reinforcement learning selects actions that maximize the state-action value function, this research demonstrates both theoretically and experimentally that selecting actions that minimize the state-action value function (counteractive actions) can achieve higher TD. The authors show that this approach leads to improved learning efficiency.\n\nSTRENGTHS AND WEAKNESSES:\n**Strengths**\n\nS1. **Novelty and Problem Importance**: The paper presents a theoretical method addressing the \"poor initial learning efficiency\" that deep RL researchers commonly experience. The authors construct theory that holds even during the early stages of learning when the Q-function is unreliable (they claim that until convergence, Q behaves almost like a random function with little correlation to immediate rewards), maximizing TD error. Specifically, Theorem 3.4 proves that minimization actions achieve TD that is D(s)-2δ-η higher than average actions.\n\nS2. **High Practicality**: The fact that this method requires zero additional computational cost and can be easily integrated into existing TD learning-based algorithms is extremely valuable in practice.\n\nS3. **Implementation from Theory to High-dimensional Experiments**: The effectiveness of the method is validated across multiple scales, from basic verification in Chain MDP to large-scale experiments in the Arcade Learning Environment. Direct measurement of temporal difference is also conducted.\n\n**Weaknesses**\n\nW1. The assumptions of η-uninformed and δ-smooth are very strong, and verification of whether these hold in actual high-dimensional environments is insufficient. In particular, these assumptions may break down as learning progresses, but the impact of this is not adequately discussed.\n\nW2. No theoretical guarantees for convergence of the proposed method are provided. The selection of minimization actions may bias exploration and potentially hinder convergence to the optimal policy, which requires investigation.\n\n**Minor Comment** The text in the figures is too small and should be enlarged.\n\nQUESTIONS:\nQ1. To clarify how well the theory holds experimentally, could you plot how long **D(s)−2δ−η>0** continues from the early stages of learning?\n\nQ2. Looking at Figure 2, the learning curve for ε-greedy appears not to have converged. When iterations are increased further, how do the final convergence scores compare between ε-greedy and CoAct TD Learning? While I understand that the proposed method accelerates convergence in early learning, I believe the convergence values are also important in practice.\n\nQ3. As an application area for reinforcement learning, LLM post-training is gaining attention. Post-training starts from a state where the LLM has completed pre-training, but would this method still be effective for improving learning efficiency in such cases? If effective, I believe this would further enhance the value of this research.\n\nLIMITATIONS:\n**Theory-Practice Gap**: The theoretical results are based on strong assumptions, and it is unclear to what extent these assumptions hold in actual complex environments.\n\n**Bias Toward Short-term Learning**: Experimental evaluation is mainly concentrated on short-term periods (100K interactions), and stability and effectiveness in long-term learning are not sufficiently verified.\n\n**Environment Dependency**: Verification is centered on the specific domain of Atari games, and generalizability to continuous control tasks and other complex domains is unclear.\n\n**Lack of Convergence Guarantees**: No theoretical convergence guarantees for the proposed method are provided, leaving questions about reliability in practical applications.", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 2, "CLARITY": 4, "IMPACT": 4, "ORIGINALITY": 4, "DATE": "2025-06-25"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThe paper introduces a straightforward yet impactful modification to experience collection in deep reinforcement learning: periodically selecting the action with the lowest Q-value. This approach, called CoAct TD Learning, accelerates learning and improves sample efficiency without adding computational overhead. The method is supported by theoretical analysis, is simple to implement, and is empirically validated on various benchmark tasks.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths\n- The paper is well written and easy to follow.\n- The idea of leveraging actions with minimum state-action values for experience collection is novel and intriguing. If the theoretical and empirical claims are substantiated, this approach could have significant impact on the field.\n\nWeaknesses\n- Some of the core assumptions underlying the proposed method, particularly those introduced in Section 3, require further explanation and justification.\n- The presentation of the experimental results should be reorganized to improve clarity.\n\nQUESTIONS:\nAssumptions in Section 3:\n- Line 128-130: The statement that \"During the large portion of the training ... while interacting with new experiences in high-dimensional MDPs as the learning continues\" is accurate for the early training stage. However, it is unclear whether this remains true as training progresses. Is there empirical or theoretical evidence to support this claim beyond the initial phase?\n- Line 131-133: The statement that \"In particular, in high-dimensional environments in a significant portion ... has little correlation with the immediate rewards.\" also needs supporting evidence or citation.\n- Theorem 3.4 depends on assumptions that \" $Q_\\theta(s, a)$ is $\\eta$ -uninformed and $\\delta$ -smooth.\" Can you provide proof or empirical evidence that these assumptions hold, especially beyond the very start of training? It appears these conditions may only be valid at initialization and not throughout training.\n\nExperimental results\n- Figure Order and Baselines: Figure 4 (canonical temporal difference learning baseline) should be presented before Figure 1 (QRDQN results), since the canonical method is a more relevant and familiar baseline for most readers. Additionally, QRDQN, while showing a larger performance gap, is less central to the paper’s main claims.\n- Figure 1 Interpretation: The performance gap between canonical $\\epsilon$ -greedy and CoAct TD emerges midway through training (after about $10^6$ environment steps), which seems at odds with the theoretical foundation that relies on random initialization and uniform action selection. This discrepancy should be addressed.\n- Figure 2 Clarity: The four subplots in Figure 2 lack informativeness; the specific $\\epsilon$ values used are not visible or clear. Furthermore, Figure 2 should appear earlier in the paper, as it is discussed before Figures 1 and 4.\n- Additional Baseline: It would strengthen the empirical evaluation to include a comparison with a purely greedy baseline, where $\\epsilon$ -greedy is not used (i.e., the agent always selects the action with the maximum Q-value).\n\nLIMITATIONS:\nSee weaknesses and questions for more detail.", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 4, "CLARITY": 3, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-07-01"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThe paper proposes a novel plug-and-play module for online reinforcement learning algorithms, called CoAct. CoAct intentionally selects the action with the lowest action value to collect novel experiences and gain information from the environment. Theoretical analysis shows that sampling the action with the minimum value increases the temporal difference (TD) error compared to uniform action sampling, leading to larger updates. \nEmpirical results demonstrate that the method outperforms $\\epsilon$-greedy, UCB,  and Noisy Networks.\n\nSTRENGTHS AND WEAKNESSES:\n**Strengths:**\n* The paper provides both theoretical analysis and empirical evaluation to support the intuition and effectiveness of the method.\n\n* The proposed method is novel and easy to use. As a plug-and-play module, CoAct can be integrated into any existing online reinforcement learning algorithm.\n\n* The empirical analysis reports both the 80th percentile and the median, offering a detailed overview of the agent’s performance. The parameter settings are reported for reproducibility. \n\n**Weaknesses**\n* The explanation remains unclear regarding how a larger TD error contributes to more effective learning. While the theoretical analysis demonstrates that selecting the action with the lowest action value leads to a high TD error, the paper lacks further analysis on how a larger TD error affects the value estimation of the true optimal action, which is ultimately what the agent should learn. After asking \"how could minimizing the state-action value function accelerate learning\", the paper states that \"the realization of the intrinsic difference between $a_{Q_{\\theta}}^{\\min}(s)$ and $a_{Q^\\*}^{\\min} (s) $ with regard to $Q_\\theta$ and $Q^\\*$ provides a valuable insight on how counteractive actions do in fact increase temporal difference.\" It is good that the paper attempts to answer this question, but it would be helpful to provide a more detailed explanation of how this insight further contributes to searching for the true $a_{Q^\\*}^{\\max}$.\n\n* The application of the method may be limited by the properties or constraints of the environment. Taking $a_{Q_\\theta}^{\\min}(s)$ may not always be applicable in practice. In online settings, every chosen action affects the environment. Since the action value estimation reflects how good an action is, taking the action with a low estimation introduces concerns about whether it causes irreversible damage to the environment, especially in real-world applications like robotics and autonomous driving. In such domains, collecting data following $a_{Q_\\theta}^{\\min}(s)$ could be unacceptable due to safety or cost concerns.\n\nQUESTIONS:\n* It would be great if the authors could explain the intuition of learning for actions with the lowest value estimation. What the agent needs to learn in the end is the action with the highest value, for deriving a good policy. Explainning the intuition more might be helpful for readers to understand the methodology.\n\n* Could the authors please clarify more about how the algorithm is evaluated? In empirical evaluation, it is not clear whether the reported performance reflects the agent’s performance during online interactions or is based on a a fixed policy rollout, i.e., whether the agent still follows $a_{Q_\\theta}^{\\min}(s)$ as the left side of Algorithm 1 indicates. Given that online actions affect the environment, I think it is important to specify this point. If following $a_{Q_\\theta}^{\\min}(s)$ is part of the learning process, then reporting the return during interactions would be more informative than only evaluating the final policy, as it captures the consequence of taking actions that are considered to be poor choices.\n\nLIMITATIONS:\nYes", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 4, "CLARITY": 3, "IMPACT": 2, "ORIGINALITY": 3, "DATE": "2025-07-02"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper proposes CoAct TD, an exploration strategy that selects the action that minimizes its current Q-value function to improve the sample efficiency of DRL. The authors provide a theoretical proof that the \"counteractive\" action generates a higher expected TD error, thereby accelerating the learning process. This theory is validated by extensive experiments on the ALE, where the method demonstrates significant performance gains, such as a 248% improvement over a standard baseline on the ALE 100K benchmark. Also, CoAct TD adds no additional computational cost and is designed as a \"plug-and-play\" module for any algorithm that relies on TD learning.\n\nSTRENGTHS AND WEAKNESSES:\n# Strengths\n- __Originality and Significance__. The core idea of using the action that minimizes the Q-function for exploration is novel. It addresses the critical problem of sample efficiency with a simple, practical solution. The reported 248% performance improvement on the ALE 100K benchmark highlights the method's potential impact.\n\n- __Quality__. The paper has a strong logical structure that flows from theory to practice. It provides a clear theoretical justification (Section 3) for its counterintuitive claim and then rigorously validates this theory with both a simple, motivating example (Section 4) and large-scale, standard benchmarks (Section 5). \n\n- __Clarity__. The paper is well-written and easy to follow.\n\n# Weaknesses\n- The theoretical proof relies on the Q-function being _\"η-uninformed\" and \"δ-smooth\" assumptions_ that hold true during the initial phases of training. However, these assumptions _no longer hold_ during the later stages of training. This raises an unanswered examination regarding the robustness of the method and its long-term convergence properties.\n\n- The experiments are limited to _discrete action_ environments employing DQN-family algorithms. The assertion of being a \"fundamental improvement to temporal difference learning\" is heavily broad; The paper fails to present evidence of its relevance to other crucial domains of reinforcement learning, such as actor-critic methods for continuous control (e.g., SAC, TD3), which also depend on TD learning.\n\nQUESTIONS:\n- The theory for CoAct TD relies on the Q-function being uninformed, a condition primarily met during early training. What is the method's behavior in late-stage training when this assumption no longer holds? A brief analysis demonstrating that the agent does not get stuck in known undesirable states would increase my confidence in the method's robustness.\n- The paper's claim of being a \"fundamental improvement\" is broad, yet experiments are confined to DQN-family algorithms. Could the authors briefly discuss how the argmin Q principle could be extended to continuous control algorithms (e.g., SAC)? \n- The paper links higher TD error to better exploration. Can the authors provide any direct evidence of improved exploration quality, such as higher state visitation entropy compared to baselines?\n\nLIMITATIONS:\nyes", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 3, "CLARITY": 3, "IMPACT": 2, "ORIGINALITY": 3, "DATE": "2025-07-03"}