Multi-level Explanation of Deep Reinforcement Learning-based Scheduling

• URL: http://arxiv.org/abs/2209.09645v1
• Date: Sun, 18 Sep 2022 13:22:53 GMT
• Title: Multi-level Explanation of Deep Reinforcement Learning-based Scheduling
• Authors: Shaojun Zhang and Chen Wang and Albert Zomaya
• Abstract summary: Dependency-aware job scheduling in the cluster is NP-hard. Recent work shows that Deep Reinforcement Learning (DRL) is capable of solving it. In this paper, we give the multi-level explanation framework to interpret the policy of DRL-based scheduling.
• Score: 3.043569093713764
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Dependency-aware job scheduling in the cluster is NP-hard. Recent work shows that Deep Reinforcement Learning (DRL) is capable of solving it. It is difficult for the administrator to understand the DRL-based policy even though it achieves remarkable performance gain. Therefore the complex model-based scheduler is not easy to gain trust in the system where simplicity is favored. In this paper, we give the multi-level explanation framework to interpret the policy of DRL-based scheduling. We dissect its decision-making process to job level and task level and approximate each level with interpretable models and rules, which align with operational practices. We show that the framework gives the system administrator insights into the state-of-the-art scheduler and reveals the robustness issue in regards to its behavior pattern.

Related papers

• Zero-Shot Instruction Following in RL via Structured LTL Representations [50.41415009303967]
  We study instruction following in multi-task reinforcement learning, where an agent must zero-shot execute novel tasks not seen during training.<n>In this setting, linear temporal logic has recently been adopted as a powerful framework for specifying structured, temporally extended tasks.<n>While existing approaches successfully train generalist policies, they often struggle to effectively capture the rich logical and temporal structure inherent in specifications.
  arXiv  Detail & Related papers  (2026-02-15T23:22:50Z)
• Sample-Efficient Neurosymbolic Deep Reinforcement Learning [49.60927398960061]
  We propose a neuro-symbolic Deep RL approach that integrates background symbolic knowledge to improve sample efficiency.<n>Online reasoning is performed to guide the training process through two mechanisms.<n>We show improved performance over a state-of-the-art reward machine baseline.
  arXiv  Detail & Related papers  (2026-01-06T09:28:53Z)
• Zero-Shot Instruction Following in RL via Structured LTL Representations [54.08661695738909]
  Linear temporal logic (LTL) is a compelling framework for specifying complex, structured tasks for reinforcement learning (RL) agents.<n>Recent work has shown that interpreting instructions as finite automata, which can be seen as high-level programs monitoring task progress, enables learning a single generalist policy capable of executing arbitrary instructions at test time.<n>We propose a novel approach to learning a multi-task policy for following arbitrary instructions that addresses this shortcoming.
  arXiv  Detail & Related papers  (2025-12-02T10:44:51Z)
• Curriculum Design for Trajectory-Constrained Agent: Compressing Chain-of-Thought Tokens in LLMs [26.165537937650413]
  Training agents to operate under strict constraints during deployment presents significant challenges.<n>We propose a curriculum learning strategy that gradually tightens constraints during training, enabling the agent to incrementally master the deployment requirements.
  arXiv  Detail & Related papers  (2025-11-04T16:14:56Z)
• Expediting Reinforcement Learning by Incorporating Knowledge About Temporal Causality in the Environment [6.914710674738284]
  Reinforcement learning algorithms struggle with learning optimal policies for tasks where reward feedback is sparse and depends on a complex sequence of events in the environment.<n>This paper proposes a novel method to incorporate causal information in the form of Temporal Logic-based Causal Diagrams into the reward formalism.
  arXiv  Detail & Related papers  (2025-10-17T09:11:26Z)
• Instructions are all you need: Self-supervised Reinforcement Learning for Instruction Following [58.60470643433354]
  Language models often struggle to follow multi-constraint instructions that are crucial for real-world applications.<n>We propose a label-free self-supervised reinforcement learning framework that eliminates dependency on external supervision.<n>Our approach introduces constraint decomposition strategies and efficient constraint-wise binary classification to address sparse reward challenges.
  arXiv  Detail & Related papers  (2025-10-16T08:24:44Z)
• Guided Policy Optimization under Partial Observability [36.853129816484845]
  Reinforcement Learning (RL) in partially observable environments poses significant challenges due to the complexity of learning under uncertainty.<n>We introduce Guided Policy Optimization (GPO), a framework that co-trains a guider and a learner.<n>We theoretically demonstrate that this learning scheme achieves optimality comparable to direct RL, thereby overcoming key limitations inherent in existing approaches.
  arXiv  Detail & Related papers  (2025-05-21T12:01:08Z)
• Sample-Efficient Reinforcement Learning with Temporal Logic Objectives: Leveraging the Task Specification to Guide Exploration [13.053013407015628]
  This paper addresses the problem of learning optimal control policies for systems with uncertain dynamics. We propose an accelerated RL algorithm that can learn control policies significantly faster than competitive approaches.
  arXiv  Detail & Related papers  (2024-10-16T00:53:41Z)
• Learning Logic Specifications for Policy Guidance in POMDPs: an Inductive Logic Programming Approach [57.788675205519986]
  We learn high-quality traces from POMDP executions generated by any solver. We exploit data- and time-efficient Indu Logic Programming (ILP) to generate interpretable belief-based policy specifications. We show that learneds expressed in Answer Set Programming (ASP) yield performance superior to neural networks and similar to optimal handcrafted task-specifics within lower computational time.
  arXiv  Detail & Related papers  (2024-02-29T15:36:01Z)
• Learning-enabled Flexible Job-shop Scheduling for Scalable Smart Manufacturing [11.509669981978874]
  In smart manufacturing systems, flexible job-shop scheduling with transportation constraints is essential to optimize solutions for maximizing productivity. Recent developments in deep reinforcement learning (DRL)-based methods for FJSPT have encountered a scale generalization challenge. We introduce a novel graph-based DRL method, named the Heterogeneous Graph Scheduler (HGS)
  arXiv  Detail & Related papers  (2024-02-14T06:49:23Z)
• Towards an Information Theoretic Framework of Context-Based Offline Meta-Reinforcement Learning [48.79569442193824]
  We show that COMRL algorithms are essentially optimizing the same mutual information objective between the task variable $M$ and its latent representation $Z$ by implementing various approximate bounds. As demonstrations, we propose a supervised and a self-supervised implementation of $I(Z; M)$, and empirically show that the corresponding optimization algorithms exhibit remarkable generalization across a broad spectrum of RL benchmarks. This work lays the information theoretic foundation for COMRL methods, leading to a better understanding of task representation learning in the context of reinforcement learning.
  arXiv  Detail & Related papers  (2024-02-04T09:58:42Z)
• Hierarchical Continual Reinforcement Learning via Large Language Model [15.837883929274758]
  Hi-Core is designed to facilitate the transfer of high-level knowledge. It orchestrates a twolayer structure: high-level policy formulation by a large language model (LLM) Hi-Core has demonstrated its effectiveness in handling diverse CRL tasks, which outperforms popular baselines.
  arXiv  Detail & Related papers  (2024-01-25T03:06:51Z)
• Semantically Aligned Task Decomposition in Multi-Agent Reinforcement Learning [56.26889258704261]
  We propose a novel "disentangled" decision-making method, Semantically Aligned task decomposition in MARL (SAMA) SAMA prompts pretrained language models with chain-of-thought that can suggest potential goals, provide suitable goal decomposition and subgoal allocation as well as self-reflection-based replanning. SAMA demonstrates considerable advantages in sample efficiency compared to state-of-the-art ASG methods.
  arXiv  Detail & Related papers  (2023-05-18T10:37:54Z)
• Compositional Reinforcement Learning from Logical Specifications [21.193231846438895]
  Recent approaches automatically generate a reward function from a given specification and use a suitable reinforcement learning algorithm to learn a policy. We develop a compositional learning approach, called DiRL, that interleaves high-level planning and reinforcement learning. Our approach then incorporates reinforcement learning to learn neural network policies for each edge (sub-task) within a Dijkstra-style planning algorithm to compute a high-level plan in the graph.
  arXiv  Detail & Related papers  (2021-06-25T22:54:28Z)
• Deep RL With Information Constrained Policies: Generalization in Continuous Control [21.46148507577606]
  We show that a natural constraint on information flow might confer onto artificial agents in continuous control tasks. We implement a novel Capacity-Limited Actor-Critic (CLAC) algorithm. Our experiments show that compared to alternative approaches, CLAC offers improvements in generalization between training and modified test environments.
  arXiv  Detail & Related papers  (2020-10-09T15:42:21Z)
• Learning Robust State Abstractions for Hidden-Parameter Block MDPs [55.31018404591743]
  We leverage ideas of common structure from the HiP-MDP setting to enable robust state abstractions inspired by Block MDPs. We derive instantiations of this new framework for both multi-task reinforcement learning (MTRL) and meta-reinforcement learning (Meta-RL) settings.
  arXiv  Detail & Related papers  (2020-07-14T17:25:27Z)
• Hierarchical Reinforcement Learning as a Model of Human Task Interleaving [60.95424607008241]
  We develop a hierarchical model of supervisory control driven by reinforcement learning. The model reproduces known empirical effects of task interleaving. The results support hierarchical RL as a plausible model of task interleaving.
  arXiv  Detail & Related papers  (2020-01-04T17:53:28Z)
• Hierarchical Variational Imitation Learning of Control Programs [131.7671843857375]
  We propose a variational inference method for imitation learning of a control policy represented by parametrized hierarchical procedures (PHP) Our method discovers the hierarchical structure in a dataset of observation-action traces of teacher demonstrations, by learning an approximate posterior distribution over the latent sequence of procedure calls and terminations. We demonstrate a novel benefit of variational inference in the context of hierarchical imitation learning: in decomposing the policy into simpler procedures, inference can leverage acausal information that is unused by other methods.
  arXiv  Detail & Related papers  (2019-12-29T08:57:02Z)
• Certified Reinforcement Learning with Logic Guidance [78.2286146954051]
  We propose a model-free RL algorithm that enables the use of Linear Temporal Logic (LTL) to formulate a goal for unknown continuous-state/action Markov Decision Processes (MDPs) The algorithm is guaranteed to synthesise a control policy whose traces satisfy the specification with maximal probability.
  arXiv  Detail & Related papers  (2019-02-02T20:09:32Z)

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.