Academic

Delayed Homomorphic Reinforcement Learning for Environments with Delayed Feedback

arXiv:2604.03641v1 Announce Type: new Abstract: Reinforcement learning in real-world systems is often accompanied by delayed feedback, which breaks the Markov assumption and impedes both learning and control. Canonical state augmentation approaches cause the state-space explosion, which introduces a severe sample-complexity burden. Despite recent progress, the state-of-the-art augmentation-based baselines remain incomplete: they either predominantly reduce the burden on the critic or adopt non-unified treatments for the actor and critic. To provide a structured and sample-efficient solution, we propose delayed homomorphic reinforcement learning (DHRL), a framework grounded in MDP homomorphisms that collapses belief-equivalent augmented states and enables efficient policy learning on the resulting abstract MDP without loss of optimality. We provide theoretical analyses of state-space compression bounds and sample complexity, and introduce a practical algorithm. Experiments on continuou

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Jongsoo Lee, Jangwon Kim, Soohee Han · · 1 min read · 14 views
#cs.LG #cs.AI

arXiv:2604.03641v1 Announce Type: new Abstract: Reinforcement learning in real-world systems is often accompanied by delayed feedback, which breaks the Markov assumption and impedes both learning and control. Canonical state augmentation approaches cause the state-space explosion, which introduces a severe sample-complexity burden. Despite recent progress, the state-of-the-art augmentation-based baselines remain incomplete: they either predominantly reduce the burden on the critic or adopt non-unified treatments for the actor and critic. To provide a structured and sample-efficient solution, we propose delayed homomorphic reinforcement learning (DHRL), a framework grounded in MDP homomorphisms that collapses belief-equivalent augmented states and enables efficient policy learning on the resulting abstract MDP without loss of optimality. We provide theoretical analyses of state-space compression bounds and sample complexity, and introduce a practical algorithm. Experiments on continuous control tasks in MuJoCo benchmark confirm that our algorithm outperforms strong augmentation-based baselines, particularly under long delays.

Executive Summary

This article presents a novel framework for reinforcement learning in real-world systems with delayed feedback, called delayed homomorphic reinforcement learning (DHRL). The proposed framework utilizes MDP homomorphisms to collapse belief-equivalent augmented states, thereby reducing the state-space explosion and sample complexity. The authors provide theoretical analyses of state-space compression bounds and sample complexity, as well as a practical algorithm. Experiments on MuJoCo benchmark tasks demonstrate that DHRL outperforms strong augmentation-based baselines, particularly under long delays. This research offers a promising solution for reinforcement learning in environments with delayed feedback. The approach aims to provide a structured and sample-efficient solution for policy learning, potentially benefiting applications in robotics, finance, and other fields.

Key Points

  • ▸ Proposed a novel framework for reinforcement learning with delayed feedback
  • ▸ Utilized MDP homomorphisms for state-space compression
  • ▸ Reduced sample complexity through efficient policy learning
  • ▸ Demonstrated outperformance over strong augmentation-based baselines
  • ▸ Provided theoretical analyses of state-space compression bounds and sample complexity

Merits

Strength

The proposed framework offers a structured and sample-efficient solution for reinforcement learning with delayed feedback, addressing a significant challenge in real-world systems.

Innovative Approach

The utilization of MDP homomorphisms for state-space compression is an innovative approach that has the potential to significantly impact the field of reinforcement learning.

Theoretical Foundation

The authors provide thorough theoretical analyses of state-space compression bounds and sample complexity, providing a solid foundation for the proposed framework.

Demerits

Limitation

The proposed framework may not be applicable to all types of delayed feedback environments, potentially limiting its scope and generalizability.

Complexity

The framework's reliance on MDP homomorphisms may introduce additional computational complexity, potentially hindering its adoption in resource-constrained environments.

Expert Commentary

The proposed framework presents a compelling solution for reinforcement learning in real-world systems with delayed feedback. The authors' innovative use of MDP homomorphisms for state-space compression offers a promising approach for reducing the sample complexity burden. However, further research is needed to fully explore the framework's limitations and potential applications. Additionally, the computational complexity of the framework may require careful consideration in resource-constrained environments. Nevertheless, this research has the potential to significantly impact the field of reinforcement learning, particularly in applications where delayed feedback is a significant challenge.

Recommendations

  • ✓ Further research is needed to explore the framework's limitations and potential applications in different domains.
  • ✓ Careful consideration should be given to the computational complexity of the framework in resource-constrained environments.

Sources

Original: arXiv - cs.LG

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