Delayed Homomorphic Reinforcement Learning for Environments with Delayed Feedback

• URL: http://arxiv.org/abs/2604.03641v1
• Date: Sat, 04 Apr 2026 08:38:52 GMT
• Title: Delayed Homomorphic Reinforcement Learning for Environments with Delayed Feedback
• Abstract summary: Delayed feedback breaks the Markov assumption and impedes learning and control.<n>We propose a framework grounded in MDP homomorphisms that collapses belief-equivalent augmented states.<n> Experiments on continuous control tasks in MuJoCo benchmark confirm that our algorithm outperforms strong augmentation-based baselines.
• Score: 11.866061471514582
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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