Delay-Empowered Causal Hierarchical Reinforcement Learning

• URL: http://arxiv.org/abs/2605.12261v1
• Date: Tue, 12 May 2026 15:28:32 GMT
• Title: Delay-Empowered Causal Hierarchical Reinforcement Learning
• Abstract summary: We propose Delay-Empowered Causal Hierarchical Reinforcement Learning (DECHRL)<n>DECHRL explicitly models both the causal structure of state transitions and their associated delay distributions.<n>We evaluate DECHRL in modified 2DMinecraft and MiniGrid environments featuring delays.
• Score: 24.381499367239858
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Many real-world tasks involve delayed effects, where the outcomes of actions emerge after varying time lags. Existing delay-aware reinforcement learning methods often rely on state augmentation, prior knowledge of delay distributions, or access to non-delayed data, limiting their generalization. Hierarchical reinforcement learning, by contrast, inherently offers advantages in handling delays due to its hierarchical structure, yet existing methods are restricted to fixed delays. To address these limitations, we propose Delay-Empowered Causal Hierarchical Reinforcement Learning (DECHRL). DECHRL explicitly models both the causal structure of state transitions and their associated stochastic delay distributions. These are then incorporated into a delay-aware empowerment objective that drives proactive exploration toward highly controllable states, thereby improving performance under temporal uncertainty. We evaluate DECHRL in modified 2D-Minecraft and MiniGrid environments featuring stochastic delays. Experimental results show that DECHRL effectively models temporal delays and significantly outperforms baselines in decision-making under temporal uncertainty.

Related papers

• Near-Optimal Stochastic Linear Bandits with Delay [47.33152077961055]
  We study linear bandits with delayed feedback under several delay models and establish near-optimal regret guarantees.<n>Our results identify when delayed linear bandits exhibit the same qualitative behavior as multi-armed bandits (MAB)<n>We show that the optimal MAB guarantee, which depends only on the delay of the optimal arm, is also unattainable in linear bandits.
  arXiv  Detail & Related papers  (2026-06-15T12:48:29Z)
• When Should a Robot Think? Resource-Aware Reasoning via Reinforcement Learning for Embodied Robotic Decision-Making [68.12864562049957]
  Embodied robotic systems increasingly rely on large language model (LLM)-based agents to support high-level reasoning.<n>This raises a fundamental question for embodied agents: when should the agent reason, and when should it act?<n>We propose RARRL (Resource-Aware Reasoning via Reinforcement Learning), a hierarchical framework for resource-aware orchestration of embodied agents.
  arXiv  Detail & Related papers  (2026-03-17T15:38:50Z)
• Reinforcement Learning via Conservative Agent for Environments with Random Delays [2.115993069505241]
  We propose a simple yet robust agent for decision-making under random delays, termed the conservative agent, which reformulates the random-delay environment into its constant-delay equivalent.<n>This enables any state-of-the-art constant-delay method to be directly extended to the random-delay environments without modifying the algorithmic structure or sacrificing performance.
  arXiv  Detail & Related papers  (2025-07-25T06:41:06Z)
• Adaptive Reinforcement Learning for Unobservable Random Delays [46.04329493317009]
  We introduce a general framework that enables agents to adaptively handle unobservable and time-varying delays.<n>Specifically, the agent generates a matrix of possible future actions to handle both unpredictable delays and lost action packets sent over networks.<n>Our method significantly outperforms state-of-the-art approaches across a wide range of benchmark environments.
  arXiv  Detail & Related papers  (2025-06-17T11:11:37Z)
• DEER: A Delay-Resilient Framework for Reinforcement Learning with Variable Delays [26.032139258562708]
  We propose $textbfDEER (Delay-resilient-Enhanced RL)$, a framework designed to effectively enhance the interpretability and address the random delay issues. In a variety of delayed scenarios, the trained encoder can seamlessly integrate with standard RL algorithms without requiring additional modifications. The results confirm that DEER is superior to state-of-the-art RL algorithms in both constant and random delay settings.
  arXiv  Detail & Related papers  (2024-06-05T09:45:26Z)
• Stochastic Approximation with Delayed Updates: Finite-Time Rates under Markovian Sampling [73.5602474095954]
  We study the non-asymptotic performance of approximation schemes with delayed updates under Markovian sampling. Our theoretical findings shed light on the finite-time effects of delays for a broad class of algorithms.
  arXiv  Detail & Related papers  (2024-02-19T03:08:02Z)
• Boosting Reinforcement Learning with Strongly Delayed Feedback Through Auxiliary Short Delays [41.52768902667611]
  Reinforcement learning (RL) is challenging in the common case of delays between events and their sensory perceptions. We present a novel Auxiliary-Delayed Reinforcement Learning (AD-RL) method that leverages auxiliary tasks involving short delays to accelerate RL with long delays. Specifically, AD-RL learns a value function for short delays and uses bootstrapping and policy improvement techniques to adjust it for long delays.
  arXiv  Detail & Related papers  (2024-02-05T16:11:03Z)
• Delays in Reinforcement Learning [2.5835347022640254]
  This dissertation aims to study the delay in the agent's observation of the state of the environment or in the execution of the agent's actions. A wide spectrum of delays will be considered, and potential solutions will be presented.
  arXiv  Detail & Related papers  (2023-09-20T07:04:46Z)
• Neural Laplace Control for Continuous-time Delayed Systems [76.81202657759222]
  We propose a continuous-time model-based offline RL method that combines a Neural Laplace dynamics model with a model predictive control (MPC) planner. We show experimentally on continuous-time delayed environments it is able to achieve near expert policy performance.
  arXiv  Detail & Related papers  (2023-02-24T12:40:28Z)
• Revisiting State Augmentation methods for Reinforcement Learning with Stochastic Delays [10.484851004093919]
  This paper formally describes the notion of Markov Decision Processes (MDPs) with delays. We show that delayed MDPs can be transformed into equivalent standard MDPs (without delays) with significantly simplified cost structure. We employ this equivalence to derive a model-free Delay-Resolved RL framework and show that even a simple RL algorithm built upon this framework achieves near-optimal rewards in environments with delays in actions and observations.
  arXiv  Detail & Related papers  (2021-08-17T10:45:55Z)
• Stochastic bandits with arm-dependent delays [102.63128271054741]
  We propose a simple but efficient UCB-based algorithm called the PatientBandits. We provide both problems-dependent and problems-independent bounds on the regret as well as performance lower bounds.
  arXiv  Detail & Related papers  (2020-06-18T12:13:58Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.