Reinforcement Learning Scheduling 2026 — PatSnap Eureka
Reinforcement Learning Scheduling: 2026 Landscape
RL-based scheduling has reached a critical inflection point as industrial complexity and cloud-scale workloads exceed the limits of handcrafted heuristics. This analysis maps 60+ patent and literature records spanning 2018–2026.
From Static Dispatch to Autonomous Self-Optimizing Schedulers
Reinforcement learning scheduling (RLS) frames the scheduling problem as a Markov Decision Process in which an agent observes system state — queue depth, resource utilization, job characteristics, network conditions — selects a scheduling action, and receives a scalar reward signal encoding throughput, makespan, latency, energy, or cost objectives.
Within this dataset, the field encompasses five identifiable sub-domains: cloud and HPC cluster job scheduling, smart manufacturing and job-shop scheduling, edge/IoT task offloading, network and communication resource scheduling, and hardware-level SoC and OS kernel scheduling. All share a common architecture: a neural network policy approximator, an environment simulator, and a reward function.
Recent filings additionally incorporate online retraining, meta-learning for rapid adaptation, and hybrid RL-heuristic switching. The three-phase innovation timeline spans a Foundational Phase (2017–2020), a Development Cluster (2021–2023) accounting for approximately 35 of 60+ records, and an Emerging Frontier (2024–2026) defined by runtime monitoring, green scheduling, and inverse RL.
Innovation is moderately concentrated in this dataset: the top 6 assignees — Adobe, Siemens, Hexagon, Bull, Samsung, and Dell — account for approximately 15 of the 40+ identifiable patent records in retrieved records, while the remaining activity is broadly distributed across universities, government labs, telecoms, and startups.
Filing Trends and Technology Cluster Distribution
Analysis of 60+ retrieved records reveals a clear acceleration from 2021 onward and a technology landscape spanning five application sub-domains, with cloud/HPC and smart manufacturing representing the largest clusters in this dataset.
RL Scheduling Patents by Application Domain (Dataset Snapshot)
Cloud/HPC scheduling and smart manufacturing are the two largest application clusters in this dataset, together accounting for the majority of identifiable patent records among the five sub-domains.
↗ Click bars to exploreRL Scheduling Innovation Timeline: Records by Phase (2017–2026)
The 2021–2023 development cluster accounts for approximately 35 of 60+ records in this dataset, representing the most active filing period; the 2024–2026 emerging frontier shows rising activity in runtime adaptation and green scheduling.
↗ Click bars to exploreKey Application Domains for RL-Based Scheduling
Within this dataset, RL scheduling patents and literature span six distinct application domains — from cloud supercomputers to NASA deep space communications — each with named assignees and measurable deployment contexts.
Cloud & HPC Cluster Scheduling
Adobe Inc. (2020, 2024) filed two US patents on self-learning cluster schedulers that iteratively refine resource request patterns to minimize contention on shared infrastructure. Bull SAS (2023–2024) filed three patents on offline RL trained on execution history databases of prior supercomputer runs, covering US and IN jurisdictions. The APER algorithm (2023 literature) further advances this domain using workflow performance metrics as adaptive priority experience replay sampling weights.
Cloud / HPCSmart Manufacturing & Job-Shop Scheduling
Siemens Aktiengesellschaft (2020–2023) filed three patents spanning WO and US jurisdictions covering real-time production scheduling with DRL and Monte Carlo tree search, plus a digital shadow RL agent deployed to production upon verified performance superiority. Samsung Display (2025) introduced inverse RL and Bayesian reward reweighting to infer implicit operator preferences across flexible job shop settings. Hexagon Technology Center GmbH (2023) filed three WO/US patents on a cloud micro-service RL engine with user feedback updating reward functions for multi-objective work scheduling.
Smart ManufacturingEdge Computing & IoT Task Offloading
Cloud Intelligence Assets Holding (Singapore, 2025) filed the first record in this dataset to explicitly combine carbon reward, electricity cost reward, and task latency reward into a single RL return signal for edge task scheduling. Lovely Professional University (India, 2025) filed an IN-jurisdiction patent on smart workspace access control and scheduling using RL. Literature from 2021–2022 documents digital twin-assisted RL approaches for edge task scheduling optimizing transmission order and energy harvesting trade-offs for IoT nodes.
Edge / IoTTelecom, Defense & Space Scheduling
Telefonaktiebolaget LM Ericsson (2022, US) filed a patent mapping RL-selected resources to pending tasks optimizing a reward function for radio resource scheduling. BAE Systems (2019, US) filed on autonomous RL-based radar scan schedule control trained on synthetic electromagnetic signal data. Literature from 2021 documents a deep RL system that generates NASA Deep Space Network spacecraft tracking schedules, replacing a 5-month manual planning process.
Telecom / DefenseLeading Patent Assignees in RL Scheduling — Dataset Snapshot
In retrieved records, Siemens Aktiengesellschaft, Hexagon Technology Center GmbH, Bull SAS, and Samsung collectively account for the largest individual filing clusters in this dataset, each with 3 patents, while Adobe, Dell, and ETRI each contribute 2 patents in retrieved records.
Top Assignees by Filing Count in Retrieved Records (Dataset Snapshot)
↗ Click bars to exploreSiemens Aktiengesellschaft
Siemens holds 3 patents across WO and US jurisdictions filed between 2020 and 2023 in this dataset. Key technology areas include real-time production scheduling combining deep RL with Monte Carlo tree search (WO/US, 2020–2021), and a digital shadow RL agent trained continuously alongside a live agent and deployed upon verified performance superiority (WO, 2023). Patent statuses span issued WO and US filings, covering flexible manufacturing system optimization.
Germany — DEHexagon Technology Center GmbH
Hexagon Technology Center GmbH holds 3 patents across WO and US jurisdictions filed in 2023 in this dataset. Key technology areas include an AI auto-scheduler and RL training framework for scheduling multiple work projects against shared resources and multiple scheduling objectives, with a cloud micro-service RL engine that incorporates user feedback to update reward functions. Filed under WO and US channels, the patents cover both the training methodology and the deployed scheduler architecture.
Switzerland — CHFive Directional Signals from 2024–2026 Filings
The most recent filings in this dataset (2024–2026) reveal five convergent technical directions: runtime coherence monitoring, live-graph scheduling with user feedback, carbon-aware multi-objective scheduling, inverse RL reward reweighting, and FPGA-level dynamic frequency scaling.
Runtime Coherence Monitoring Closes the Open-Loop Gap
The Wisconsin Alumni Research Foundation’s 2026 WO patent introduces gradient coherence computation as a runtime signal to detect when a deployed RL policy has drifted from its training distribution. This triggers automatic incremental retraining, addressing the silent degradation problem common in deployed RL schedulers for domain-specific SoC systems. This filing defines a distinct IP surface around runtime monitoring that R&D teams should evaluate before deploying RL schedulers in production.
Live-Graph Scheduling with Continuous User Preference Integration
Kinaxis Inc.’s April 2026 pending US patent combines a dynamic graph representation of scheduling dependencies — with nodes representing machines and jobs, and edges encoding compatibility, dependencies, and constraints — with a continuous user feedback loop. User feedback on generated schedules updates both a data profiler and agent weights in production, enabling the agent to adapt to evolving operator preferences without offline retraining cycles. This is the first retrieved filing to combine both mechanisms in a live environment.
Deep Policy Gradient vs. Multi-Agent Hierarchical RL Schedulers
Click any row to explore further.
| Dimension | Deep Policy Gradient / Actor-Critic | Multi-Agent / Hierarchical RL |
|---|---|---|
| Core Architecture | Single agent with actor-critic (A2C, PPO, DDPG) or DQN; continuous or discrete action space | Multiple agents managing sub-problems (e.g., external share, internal allocation, leftover redistribution) or hierarchical global-local decomposition |
| Representative Patent | Samsung Electronics hybrid scheduling for DL workloads (US, 2022): actor generates actions, critic evaluates; hybrid RL/heuristic selection per task | ETRI altruistic scheduling (US, 2023): three-agent decomposition — external agent, internal agent, leftover agent |
| State Encoding | Encoded state vectors describing job queues and resource availability fed to a unified neural network policy approximator | Separate state observations per agent or hierarchical sub-state decomposition; agents may share partial observations |
| Scheduling Objective | Single or weighted combined reward: throughput, makespan, latency, energy, or cost encoded in scalar return | Multi-objective or multi-resource optimization decomposed across agents; supports fairness and residual redistribution via specialist agents |
| Hybrid Fallback | Common in commercial filings (Samsung, Adobe, Hexagon): RL output blended with or switched against heuristic-generated schedules at runtime | MARS (2022) uses ensemble of pre-trained heuristic-workload models with cost-aware actor-critic selecting among backfilling, SJF, and DNN strategies |
| Adaptation Mechanism | Online retraining triggered by runtime coherence monitoring (Wisconsin Alumni Research Foundation, 2026 WO); importance sampling for policy transfer | User feedback updates data profiler and agent weights in production (Kinaxis, 2026 US pending); meta-gradient RL for rapid re-adaptation to distribution shift |
| Primary Application Domain | Cloud/HPC cluster scheduling, deep learning workload scheduling, IT asset lifecycle management | Smart manufacturing (flexible job shop, hybrid flow shop), multi-resource allocation, multi-objective production scheduling |
FAQ: Reinforcement Learning Scheduling Patents 2026
RL scheduling frames the problem as a Markov Decision Process in which an agent observes system state, selects scheduling actions, and receives scalar reward signals. Through repeated trial-and-error interaction accelerated by deep neural networks, the agent learns a policy that generalizes across unseen scheduling scenarios without explicit rule engineering — unlike static FIFO, SJF, or EASY backfilling heuristics.
Cloud and HPC cluster scheduling is the largest application cluster, followed by smart manufacturing and job-shop scheduling. Additional domains include edge/IoT task offloading, telecommunications and network resource scheduling, IT infrastructure management, and space and defense scheduling — all represented by named assignees in this dataset.
The 2026 WO patent introduces gradient coherence computation as a runtime signal to detect when a deployed RL policy has drifted from its training distribution, automatically triggering incremental retraining. This addresses the silent policy degradation problem in deployed RL schedulers for domain-specific SoC systems.
Samsung Display’s 2025 US pending patent introduces a policy combination algorithm that trains multiple policies under different reward weights, then uses inverse RL or Bayesian optimization to determine the optimal weight for a combined third policy. This allows schedulers to infer implicit operator preferences rather than requiring manually specified reward functions.
Siemens Aktiengesellschaft, Hexagon Technology Center GmbH, Bull SAS, and Samsung Electronics/Display each have approximately 3 patents in this dataset. Adobe Inc., Dell Products L.P., and Electronics and Telecommunications Research Institute each have 2 patents in retrieved records.
Cloud Intelligence Assets Holding’s 2025 Singapore filing is the first in this dataset to explicitly combine carbon reward, electricity cost reward, and task latency reward into a single RL return signal. According to the content, this reflects anticipated enterprise and public-sector procurement requirements tied to Scope 2/3 emissions reporting, signaling that early IP positions in multi-objective green scheduling will carry increasing commercial weight.
Data and insights on this page are based on a limited patent and literature dataset and are for reference only. Figures may not represent the complete technology landscape.