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Revisiting Mixture Policies in Entropy-Regularized Actor-Critic

ICLR 2026 Conference SubmissionAnonymous Authors
OpenReview Score: 6.0Download Report PDF
policy parameterizationreparameterizationentropy regularizationactor-criticpolicy optimizationexplorationcontinuous controlreinforcement learning
Abstract:

Mixture policies in reinforcement learning offer greater flexibility compared to their base component policies. We demonstrate that this flexibility, in theory, enhances solution quality and improves robustness to the entropy scale. Despite these advantages, mixtures are rarely used in algorithms like Soft Actor-Critic, and the few empirical studies that are available do not show their effectiveness. One possible explanation is that base policies, like Gaussian policies, admit a reparameterization that enables low-variance gradient updates, whereas mixtures do not. To address this, we introduce a marginalized reparameterization (MRP) estimator for mixture policies that has provably lower variance than the standard likelihood-ratio (LR) estimator. We conduct extensive experiments across a large suite of synthetic bandits and environments from classic control, Gym MuJoCo, DeepMind Control Suite, MetaWorld, and MyoSuite. Our results show, for the first time, that mixture policies trained with our MRP estimator are more stable than the LR variant and are competitive compared to Gaussian policies across many benchmarks. In addition, our approach shows benefits when the critic surface is multimodal and in tasks with unshaped rewards.

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This report is AI-GENERATED using Large Language Models and WisPaper (A scholar search engine). It analyzes academic papers' tasks and contributions against retrieved prior work. While this system identifies POTENTIAL overlaps and novel directions, ITS COVERAGE IS NOT EXHAUSTIVE AND JUDGMENTS ARE APPROXIMATE. These results are intended to assist human reviewers and SHOULD NOT be relied upon as a definitive verdict on novelty.
NOTE that some papers exist in multiple, slightly different versions (e.g., with different titles or URLs). The system may retrieve several versions of the same underlying work. The current automated pipeline does not reliably align or distinguish these cases, so human reviewers will need to disambiguate them manually.
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Overview

Overall Novelty Assessment

The paper introduces a marginalized reparameterization estimator for mixture policies in entropy-regularized actor-critic reinforcement learning, addressing the variance challenges that have historically limited mixture policy adoption. It resides in the 'Mixture Policy Formulations and Gradient Estimation' leaf, which contains four papers total including this work. This leaf represents a focused research direction within the broader single-agent entropy-regularized methods branch, suggesting a moderately sparse area where foundational questions about mixture policy gradient estimation remain active.

The taxonomy reveals that this work sits at the methodological core of single-agent entropy-regularized methods, the densest branch in the field. Neighboring leaves address off-policy sample efficiency, exploration through multi-objective optimization, and maximum entropy framework applications. The scope notes indicate clear boundaries: methods without explicit mixture formulations belong elsewhere, while multi-agent coordination falls under a separate branch. The three sibling papers in this leaf tackle related gradient estimation and mixture formulation challenges, though the taxonomy narrative suggests they differ in whether they emphasize maximum-entropy frameworks or adaptive weighting schemes.

Among the three contributions analyzed, none were clearly refuted by the 26 candidates examined. The marginalized reparameterization estimator examined 6 candidates with 0 refutable matches, the theoretical robustness analysis examined 10 candidates with 0 refutable matches, and the empirical demonstration examined 10 candidates with 0 refutable matches. This suggests that within the limited search scope, the specific combination of variance reduction for mixture policies via marginalized reparameterization appears relatively unexplored, though the broader themes of mixture policies and entropy regularization have established prior work in the field.

Based on the top-26 semantic matches and the taxonomy structure, the work appears to address a recognized gap in a moderately active research direction. The analysis covers gradient estimation techniques and mixture policy formulations but does not exhaustively survey all variance reduction methods or alternative policy parameterizations in reinforcement learning. The absence of refutable candidates among examined papers suggests the specific technical approach may be novel within the scope analyzed, though the broader problem of making mixture policies practical has been acknowledged in prior literature.

Taxonomy

Core-task Taxonomy Papers
14
3
Claimed Contributions
26
Contribution Candidate Papers Compared
0
Refutable Paper

Research Landscape Overview

Core task: Mixture policies in entropy-regularized actor-critic reinforcement learning. The field centers on leveraging entropy regularization to encourage exploration and robustness in policy learning, with the taxonomy revealing three main branches. Single-Agent Entropy-Regularized Methods form the densest branch, encompassing foundational approaches that blend mixture policy formulations with gradient estimation techniques, as well as variants that adapt entropy coefficients or incorporate relative entropy constraints. Multi-Agent Entropy-Regularized Methods extend these ideas to cooperative or competitive settings, where adaptive entropy schedules help balance individual agent exploration with team coordination, as seen in works like Adaptive Entropy MultiAgent[1]. Federated and Distributed Learning represents a smaller but growing branch, addressing scenarios where policy updates must be aggregated across decentralized agents or data sources, exemplified by Federated Natural Policy[2]. Together, these branches illustrate how entropy regularization serves as a unifying principle across diverse problem settings, from single-agent control to multi-agent coordination and distributed optimization. Within the single-agent branch, a particularly active line of work focuses on mixture policy formulations and gradient estimation, exploring how to combine multiple policy components or skill primitives while maintaining tractable entropy-regularized objectives. Mixture Policies Entropy[0] sits squarely in this cluster, emphasizing the theoretical and algorithmic challenges of estimating gradients when policies are expressed as mixtures. Nearby, MaxEnt Mixture Policies[10] and SAC AWMP[6] tackle similar questions, though they differ in whether they prioritize maximum-entropy frameworks or adaptive weighting schemes for mixture components. Another contrasting theme emerges in works like Implicit Credit Assignment[3] and Entropy Blending Policies[5], which address how to attribute credit or blend exploration strategies without explicit mixture structures. The original paper's focus on rigorous gradient estimation for mixture policies places it at the methodological core of this subfield, bridging foundational entropy-regularized actor-critic methods with more recent efforts to scale exploration and skill composition.

Claimed Contributions

Marginalized Reparameterization (MRP) Estimator for Mixture Policies

The authors propose a new gradient estimator for mixture policies that marginalizes over mixing weights. This estimator is proven to have lower variance than the likelihood-ratio estimator and enables effective reparameterization-based training of mixture policies in entropy-regularized actor-critic algorithms.

6 retrieved papers
Theoretical Analysis of Mixture Policy Robustness to Entropy Regularization

The authors prove that mixture policies achieve comparable or better objective values than base policies and are more robust to larger entropy regularization. They show that stationary points may not exist for Gaussian policies under strong entropy regularization but do exist for Gaussian mixture policies.

10 retrieved papers
Empirical Demonstration of Mixture Policy Effectiveness

The authors conduct extensive experiments across synthetic bandits and multiple continuous control benchmarks demonstrating that mixture policies with the MRP estimator are competitive with Gaussian policies and show particular benefits in multimodal critic surfaces and tasks with unshaped rewards.

10 retrieved papers

Core Task Comparisons

Comparisons with papers in the same taxonomy category

Contribution Analysis

Detailed comparisons for each claimed contribution

Contribution

Marginalized Reparameterization (MRP) Estimator for Mixture Policies

The authors propose a new gradient estimator for mixture policies that marginalizes over mixing weights. This estimator is proven to have lower variance than the likelihood-ratio estimator and enables effective reparameterization-based training of mixture policies in entropy-regularized actor-critic algorithms.

Contribution

Theoretical Analysis of Mixture Policy Robustness to Entropy Regularization

The authors prove that mixture policies achieve comparable or better objective values than base policies and are more robust to larger entropy regularization. They show that stationary points may not exist for Gaussian policies under strong entropy regularization but do exist for Gaussian mixture policies.

Contribution

Empirical Demonstration of Mixture Policy Effectiveness

The authors conduct extensive experiments across synthetic bandits and multiple continuous control benchmarks demonstrating that mixture policies with the MRP estimator are competitive with Gaussian policies and show particular benefits in multimodal critic surfaces and tasks with unshaped rewards.

Table of Contents