---
_id: '7175'
abstract:
- lang: eng
  text: Deep reinforcement learning (RL) policies for robotic control typically overfit
    to a single hardware configuration. Any change to the kinematic chain breaks the
    learned mapping and requires complete retraining. This paper investigates whether
    combining self-attention mechanisms with explicit spatial observations can improve
    a policy’s adaptability to varying kinematic topologies. We train a single RL
    agent to control a robotic manipulator across different degrees of freedom (DOF),
    ranging from a restricted 4-DOF mode to a fully redundant 7-DOF configuration.
    Instead of fixed-length state vectors, the method processes the active joints
    as a variable-length sequence in an attention buffer, enriching each joint’s representation
    with its relative spatial routing and geometric Jacobian influence. This structure
    allows the agent to dynamically evaluate the physical utility of its available
    actuators. The proposed Architecture reaches a success rate of 82.1% across trained
    topologies and 66.6% on unseen configurations, outperforming MLP and generic attention
    baselines while remaining nearly collisionfree. Finally, we demonstrate successful
    sim-to-real transfer by deploying the simulation-trained agent on a physical Franka
    Emika Panda manipulator.
author:
- first_name: Patrick Thomas
  full_name: Mayer, Patrick Thomas
  id: '244831'
  last_name: Mayer
  orcid: 0009-0009-6800-1273
  orcid_put_code_url: https://api.orcid.org/v2.0/0009-0009-6800-1273/work/227375532
- first_name: Jan
  full_name: Rexilius, Jan
  id: '245736'
  last_name: Rexilius
  orcid: 0000-0002-4579-214X
  orcid_put_code_url: https://api.orcid.org/v2.0/0000-0002-4579-214X/work/227375533
citation:
  alphadin: '<span style="font-variant:small-caps;">Mayer, Patrick Thomas</span> ;
    <span style="font-variant:small-caps;">Rexilius, Jan</span>: Adapting to Variable
    Kinematic Configurations: A Causal-Kinematic Attention Approach for Robotic Control.
    In:'
  ama: 'Mayer PT, Rexilius J. Adapting to Variable Kinematic Configurations: A Causal-Kinematic
    Attention Approach for Robotic Control.'
  apa: 'Mayer, P. T., &#38; Rexilius, J. (n.d.). Adapting to Variable Kinematic Configurations:
    A Causal-Kinematic Attention Approach for Robotic Control. Presented at the 9th
    Iberian Robotics Conference (ROBOT) , Barcelona.'
  bibtex: '@inproceedings{Mayer_Rexilius, title={Adapting to Variable Kinematic Configurations:
    A Causal-Kinematic Attention Approach for Robotic Control}, author={Mayer, Patrick
    Thomas and Rexilius, Jan} }'
  chicago: 'Mayer, Patrick Thomas, and Jan Rexilius. “Adapting to Variable Kinematic
    Configurations: A Causal-Kinematic Attention Approach for Robotic Control,” n.d.'
  ieee: 'P. T. Mayer and J. Rexilius, “Adapting to Variable Kinematic Configurations:
    A Causal-Kinematic Attention Approach for Robotic Control,” presented at the 9th
    Iberian Robotics Conference (ROBOT) , Barcelona.'
  mla: 'Mayer, Patrick Thomas, and Jan Rexilius. <i>Adapting to Variable Kinematic
    Configurations: A Causal-Kinematic Attention Approach for Robotic Control</i>.'
  short: 'P.T. Mayer, J. Rexilius, in: n.d.'
conference:
  end_date: 2026-11-20
  location: Barcelona
  name: '9th Iberian Robotics Conference (ROBOT) '
  start_date: 2026-11-18
date_created: 2026-09-21T16:34:09Z
date_updated: 2026-09-21T19:31:42Z
department:
- _id: '102'
keyword:
- Reinforcement Learning
- Robotic Manipulation
- Morphology Generalization
- Sim-to-Real Transfer
language:
- iso: eng
project:
- _id: A827C0AA-C7DA-11E9-B0AE-1F4CB252D58D
  name: Institute for Building Intelligence
publication_status: accepted
quality_controlled: '1'
status: public
title: 'Adapting to Variable Kinematic Configurations: A Causal-Kinematic Attention
  Approach for Robotic Control'
type: conference
user_id: '244831'
year: '2026'
...
---
_id: '7174'
abstract:
- lang: eng
  text: This paper presents a hybrid control architecture for dynamic robotic picking
    tasks. The framework combines a Deep Reinforcement Learning policy for high-level
    interception with a dedicated Inverse Kinematics controller for precise terminal
    grasping, while mitigating precision limitations of monolithic learning-based
    approaches. The framework utilizes a Proximal Policy Optimization agent to approach
    moving targets, seamlessly transitioning to an Inverse Kinematics solver that
    reduces terminal orientational and positional errors while minimizing cumulative
    control effort. To facilitate deployment on physical hardware, a robust sim-to-real
    pipeline incorporating system identification, domain randomization, and latency
    injection is employed. Experimental results on a physical Franka Emika Panda manipulator
    validate this hybrid architecture. The system achieves an 80% success rate in
    pick-and-place tasks, compared to 60.8% for unadapted baselines, with no safety-critical
    violations such as joint limit breaches or collisions observed during testing.
author:
- first_name: Patrick Thomas
  full_name: Mayer, Patrick Thomas
  id: '244831'
  last_name: Mayer
  orcid: 0009-0009-6800-1273
  orcid_put_code_url: https://api.orcid.org/v2.0/0009-0009-6800-1273/work/227375528
- first_name: Jan
  full_name: Rexilius, Jan
  id: '245736'
  last_name: Rexilius
  orcid: 0000-0002-4579-214X
  orcid_put_code_url: https://api.orcid.org/v2.0/0000-0002-4579-214X/work/227375529
citation:
  alphadin: '<span style="font-variant:small-caps;">Mayer, Patrick Thomas</span> ;
    <span style="font-variant:small-caps;">Rexilius, Jan</span>: A Hybrid Control
    Framework Using Reinforcement Learning for Dynamic Robotic Manipulation and Sim-to-Real
    Transfer. In:'
  ama: Mayer PT, Rexilius J. A Hybrid Control Framework Using Reinforcement Learning
    for Dynamic Robotic Manipulation and Sim-to-Real Transfer.
  apa: Mayer, P. T., &#38; Rexilius, J. (n.d.). A Hybrid Control Framework Using Reinforcement
    Learning for Dynamic Robotic Manipulation and Sim-to-Real Transfer. Presented
    at the 26th International Conference on Control, Automation and Systems (ICCAS),
    Sapporo.
  bibtex: '@inproceedings{Mayer_Rexilius, title={A Hybrid Control Framework Using
    Reinforcement Learning for Dynamic Robotic Manipulation and Sim-to-Real Transfer},
    author={Mayer, Patrick Thomas and Rexilius, Jan} }'
  chicago: Mayer, Patrick Thomas, and Jan Rexilius. “A Hybrid Control Framework Using
    Reinforcement Learning for Dynamic Robotic Manipulation and Sim-to-Real Transfer,”
    n.d.
  ieee: P. T. Mayer and J. Rexilius, “A Hybrid Control Framework Using Reinforcement
    Learning for Dynamic Robotic Manipulation and Sim-to-Real Transfer,” presented
    at the 26th International Conference on Control, Automation and Systems (ICCAS),
    Sapporo.
  mla: Mayer, Patrick Thomas, and Jan Rexilius. <i>A Hybrid Control Framework Using
    Reinforcement Learning for Dynamic Robotic Manipulation and Sim-to-Real Transfer</i>.
  short: 'P.T. Mayer, J. Rexilius, in: n.d.'
conference:
  end_date: 2026-10-30
  location: Sapporo
  name: 26th International Conference on Control, Automation and Systems (ICCAS)
  start_date: 2026-10-27
date_created: 2026-09-21T16:30:06Z
date_updated: 2026-09-21T19:31:37Z
department:
- _id: '102'
keyword:
- Reinforcement Learning
- Hybrid Control
- Sim-to-Real
- Robotic Manipulation
- Inverse Kinematics.
language:
- iso: eng
project:
- _id: A827C0AA-C7DA-11E9-B0AE-1F4CB252D58D
  name: Institute for Building Intelligence
publication_status: accepted
quality_controlled: '1'
status: public
title: A Hybrid Control Framework Using Reinforcement Learning for Dynamic Robotic
  Manipulation and Sim-to-Real Transfer
type: conference
user_id: '244831'
year: '2026'
...
---
_id: '7173'
abstract:
- lang: eng
  text: Reinforcement learning has achieved state-of-the-art performance in MAV control,
    waypoint flight, and obstacle avoidance. However, existing RL approaches often
    assume fixed objectives and constraints, with flight behavior largely limited
    by vehicle dynamics and orientation considered only when required for locomotion.
    Classical planning and model predictive control handle such constraints explicitly,
    but require optimization or replanning. This motivates methods that combine learned
    local control with explicit constraint handling. We combine reinforcement learning
    with control barrier functions to improve constraint-aware execution. We propose
    parameterized waypoints that encode orientation, velocity, and corridor constraints.
    Simulation and real-world experiments show that a single policy can execute different
    constraint-parameterized navigation scenarios, revealing scenario-dependent trade-offs
    between traversal time, tracking accuracy, and constraint satisfaction.
author:
- first_name: André
  full_name: Kirsch, André
  id: '229807'
  last_name: Kirsch
- first_name: Jan
  full_name: Rexilius, Jan
  id: '245736'
  last_name: Rexilius
  orcid: 0000-0002-4579-214X
  orcid_put_code_url: https://api.orcid.org/v2.0/0000-0002-4579-214X/work/227345663
citation:
  alphadin: '<span style="font-variant:small-caps;">Kirsch, André</span> ; <span style="font-variant:small-caps;">Rexilius,
    Jan</span>: Reinforcement Learning-based MAV Navigation With Parameterized Waypoints:
    Incorporating Orientation, Speed Limits, and Corridor Constraints. In:'
  ama: 'Kirsch A, Rexilius J. Reinforcement Learning-based MAV Navigation With Parameterized
    Waypoints: Incorporating Orientation, Speed Limits, and Corridor Constraints.'
  apa: 'Kirsch, A., &#38; Rexilius, J. (n.d.). Reinforcement Learning-based MAV Navigation
    With Parameterized Waypoints: Incorporating Orientation, Speed Limits, and Corridor
    Constraints. Presented at the 9th Iberian Robotics Conference (ROBOT), Barcelona.'
  bibtex: '@inproceedings{Kirsch_Rexilius, title={Reinforcement Learning-based MAV
    Navigation With Parameterized Waypoints: Incorporating Orientation, Speed Limits,
    and Corridor Constraints}, author={Kirsch, André and Rexilius, Jan} }'
  chicago: 'Kirsch, André, and Jan Rexilius. “Reinforcement Learning-Based MAV Navigation
    With Parameterized Waypoints: Incorporating Orientation, Speed Limits, and Corridor
    Constraints,” n.d.'
  ieee: 'A. Kirsch and J. Rexilius, “Reinforcement Learning-based MAV Navigation With
    Parameterized Waypoints: Incorporating Orientation, Speed Limits, and Corridor
    Constraints,” presented at the 9th Iberian Robotics Conference (ROBOT), Barcelona.'
  mla: 'Kirsch, André, and Jan Rexilius. <i>Reinforcement Learning-Based MAV Navigation
    With Parameterized Waypoints: Incorporating Orientation, Speed Limits, and Corridor
    Constraints</i>.'
  short: 'A. Kirsch, J. Rexilius, in: n.d.'
conference:
  end_date: 2026-11-20
  location: Barcelona
  name: 9th Iberian Robotics Conference (ROBOT)
  start_date: 2026-11-18
date_created: 2026-09-21T14:31:38Z
date_updated: 2026-09-21T14:33:10Z
keyword:
- MAV navigation
- Reinforcement learning
- Constrained navigation
- Control barrier functions
language:
- iso: eng
project:
- _id: A827C0AA-C7DA-11E9-B0AE-1F4CB252D58D
  name: Institute for Building Intelligence
publication_status: accepted
quality_controlled: '1'
status: public
title: 'Reinforcement Learning-based MAV Navigation With Parameterized Waypoints:
  Incorporating Orientation, Speed Limits, and Corridor Constraints'
type: conference
user_id: '229807'
year: '2026'
...
---
_id: '4392'
alternative_id:
- '5468'
author:
- first_name: Felix
  full_name: Grumbach, Felix
  id: '243801'
  last_name: Grumbach
  orcid: 0000-0001-6348-7897
  orcid_put_code_url: https://api.orcid.org/v2.0/0000-0001-6348-7897/work/156390666
citation:
  alphadin: '<span style="font-variant:small-caps;">Grumbach, Felix</span>: <i>Feldsynchrone
    Ablaufplanung dynamischer Fertigungsprozesse mit Techniken des maschinellen Lernens
    [kumulative Dissertation]</i>. Bernburg : Universitäts- und Landesbibliothek Sachsen-Anhalt,
    2024'
  ama: 'Grumbach F. <i>Feldsynchrone Ablaufplanung dynamischer Fertigungsprozesse
    mit Techniken des maschinellen Lernens [kumulative Dissertation]</i>. Bernburg:
    Universitäts- und Landesbibliothek Sachsen-Anhalt; 2024. doi:<a href="https://doi.org/10.25673/115290">10.25673/115290</a>'
  apa: 'Grumbach, F. (2024). <i>Feldsynchrone Ablaufplanung dynamischer Fertigungsprozesse
    mit Techniken des maschinellen Lernens [kumulative Dissertation]</i>. Bernburg:
    Universitäts- und Landesbibliothek Sachsen-Anhalt. <a href="https://doi.org/10.25673/115290">https://doi.org/10.25673/115290</a>'
  bibtex: '@book{Grumbach_2024, place={Bernburg}, title={Feldsynchrone Ablaufplanung
    dynamischer Fertigungsprozesse mit Techniken des maschinellen Lernens [kumulative
    Dissertation]}, DOI={<a href="https://doi.org/10.25673/115290">10.25673/115290</a>},
    publisher={Universitäts- und Landesbibliothek Sachsen-Anhalt}, author={Grumbach,
    Felix}, year={2024} }'
  chicago: 'Grumbach, Felix. <i>Feldsynchrone Ablaufplanung dynamischer Fertigungsprozesse
    mit Techniken des maschinellen Lernens [kumulative Dissertation]</i>. Bernburg:
    Universitäts- und Landesbibliothek Sachsen-Anhalt, 2024. <a href="https://doi.org/10.25673/115290">https://doi.org/10.25673/115290</a>.'
  ieee: 'F. Grumbach, <i>Feldsynchrone Ablaufplanung dynamischer Fertigungsprozesse
    mit Techniken des maschinellen Lernens [kumulative Dissertation]</i>. Bernburg:
    Universitäts- und Landesbibliothek Sachsen-Anhalt, 2024.'
  mla: Grumbach, Felix. <i>Feldsynchrone Ablaufplanung dynamischer Fertigungsprozesse
    mit Techniken des maschinellen Lernens [kumulative Dissertation]</i>. Universitäts-
    und Landesbibliothek Sachsen-Anhalt, 2024, doi:<a href="https://doi.org/10.25673/115290">10.25673/115290</a>.
  short: F. Grumbach, Feldsynchrone Ablaufplanung dynamischer Fertigungsprozesse mit
    Techniken des maschinellen Lernens [kumulative Dissertation], Universitäts- und
    Landesbibliothek Sachsen-Anhalt, Bernburg, 2024.
date_created: 2024-03-14T11:32:15Z
date_updated: 2026-07-06T12:23:58Z
doi: 10.25673/115290
file:
- access_level: open_access
  content_type: application/pdf
  creator: fgrumbach1
  date_created: 2024-03-14T11:32:13Z
  date_updated: 2024-03-14T11:32:13Z
  file_id: '4393'
  file_name: Diss_FGrumbach_2024_Final.pdf
  file_size: 7859995
  relation: main_file
  success: 1
file_date_updated: 2024-03-14T11:32:13Z
has_accepted_license: '1'
keyword:
- Produktionsplanung und -steuerung
- Operations Research
- Machine Learning
- Reinforcement Learning
language:
- iso: ger
main_file_link:
- open_access: '1'
  url: http://dx.doi.org/10.25673/115290
oa: '1'
place: Bernburg
publication_identifier:
  eisbn:
  - 978-3-96057-174-2
publication_status: published
publisher: Universitäts- und Landesbibliothek Sachsen-Anhalt
status: public
supervisor:
- first_name: Sebastian
  full_name: Trojahn, Sebastian
  last_name: Trojahn
title: Feldsynchrone Ablaufplanung dynamischer Fertigungsprozesse mit Techniken des
  maschinellen Lernens [kumulative Dissertation]
tmp:
  image: /images/cc_by.png
  legal_code_url: https://creativecommons.org/licenses/by/4.0/legalcode
  name: Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)
  short: CC BY (4.0)
type: dissertation
urn: urn:nbn:de:hbz:bi10-43927
user_id: '33976'
year: '2024'
...
---
_id: '2602'
article_number: '983'
article_type: review
author:
- first_name: Niklas
  full_name: Panneke, Niklas
  last_name: Panneke
- first_name: Andrea
  full_name: Ehrmann, Andrea
  id: '223776'
  last_name: Ehrmann
  orcid: 0000-0003-0695-3905
  orcid_put_code_url: https://api.orcid.org/v2.0/0000-0003-0695-3905/work/181737412
citation:
  alphadin: '<span style="font-variant:small-caps;">Panneke, Niklas</span> ; <span
    style="font-variant:small-caps;">Ehrmann, Andrea</span>: Stab-Resistant Polymers—Recent
    Developments in Materials and Structures. In: <i>Polymers</i> Bd. 15, MDPI AG
    (2023), Nr. 4'
  ama: Panneke N, Ehrmann A. Stab-Resistant Polymers—Recent Developments in Materials
    and Structures. <i>Polymers</i>. 2023;15(4). doi:<a href="https://doi.org/10.3390/polym15040983">10.3390/polym15040983</a>
  apa: Panneke, N., &#38; Ehrmann, A. (2023). Stab-Resistant Polymers—Recent Developments
    in Materials and Structures. <i>Polymers</i>, <i>15</i>(4). <a href="https://doi.org/10.3390/polym15040983">https://doi.org/10.3390/polym15040983</a>
  bibtex: '@article{Panneke_Ehrmann_2023, title={Stab-Resistant Polymers—Recent Developments
    in Materials and Structures}, volume={15}, DOI={<a href="https://doi.org/10.3390/polym15040983">10.3390/polym15040983</a>},
    number={4983}, journal={Polymers}, publisher={MDPI AG}, author={Panneke, Niklas
    and Ehrmann, Andrea}, year={2023} }'
  chicago: Panneke, Niklas, and Andrea Ehrmann. “Stab-Resistant Polymers—Recent Developments
    in Materials and Structures.” <i>Polymers</i> 15, no. 4 (2023). <a href="https://doi.org/10.3390/polym15040983">https://doi.org/10.3390/polym15040983</a>.
  ieee: N. Panneke and A. Ehrmann, “Stab-Resistant Polymers—Recent Developments in
    Materials and Structures,” <i>Polymers</i>, vol. 15, no. 4, 2023.
  mla: Panneke, Niklas, and Andrea Ehrmann. “Stab-Resistant Polymers—Recent Developments
    in Materials and Structures.” <i>Polymers</i>, vol. 15, no. 4, 983, MDPI AG, 2023,
    doi:<a href="https://doi.org/10.3390/polym15040983">10.3390/polym15040983</a>.
  short: N. Panneke, A. Ehrmann, Polymers 15 (2023).
date_created: 2023-03-15T15:59:16Z
date_updated: 2026-05-19T14:08:33Z
doi: 10.3390/polym15040983
file:
- access_level: open_access
  content_type: application/pdf
  creator: aehrmann
  date_created: 2023-03-15T15:58:24Z
  date_updated: 2023-03-15T15:58:24Z
  file_id: '2603'
  file_name: _2023_Panneke_Polymers15_983.pdf
  file_size: 7726320
  relation: main_file
  success: 1
file_date_updated: 2023-03-15T15:58:24Z
funded_apc: '1'
has_accepted_license: '1'
intvolume: '        15'
issue: '4'
keyword:
- body armor
- additive manufacturing
- functional textiles
- sensory textiles
- shear-thickening fluid
- reinforcement
- stab protection
- VPAM-KDIW
- HOSDB
language:
- iso: eng
main_file_link:
- open_access: '1'
oa: '1'
publication: Polymers
publication_identifier:
  eissn:
  - 2073-4360
publication_status: published
publisher: MDPI AG
quality_controlled: '1'
status: public
title: Stab-Resistant Polymers—Recent Developments in Materials and Structures
tmp:
  image: /images/cc_by.png
  legal_code_url: https://creativecommons.org/licenses/by/4.0/legalcode
  name: Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)
  short: CC BY (4.0)
type: journal_article
urn: urn:nbn:de:hbz:bi10-26025
user_id: '250307'
volume: 15
year: '2023'
...
---
_id: '2295'
article_type: original
author:
- first_name: Felix
  full_name: Grumbach, Felix
  id: '243801'
  last_name: Grumbach
  orcid: 0000-0001-6348-7897
  orcid_put_code_url: https://api.orcid.org/v2.0/0000-0001-6348-7897/work/218753400
- first_name: Anna
  full_name: Müller, Anna
  last_name: Müller
- first_name: Pascal
  full_name: Reusch, Pascal
  last_name: Reusch
- first_name: Sebastian
  full_name: Trojahn, Sebastian
  last_name: Trojahn
citation:
  alphadin: '<span style="font-variant:small-caps;">Grumbach, Felix</span> ; <span
    style="font-variant:small-caps;">Müller, Anna</span> ; <span style="font-variant:small-caps;">Reusch,
    Pascal</span> ; <span style="font-variant:small-caps;">Trojahn, Sebastian</span>:
    Robust-stable scheduling in dynamic flow shops based on deep reinforcement learning.
    In: <i>Journal of Intelligent Manufacturing</i>, Springer Science and Business
    Media LLC (2022)'
  ama: Grumbach F, Müller A, Reusch P, Trojahn S. Robust-stable scheduling in dynamic
    flow shops based on deep reinforcement learning. <i>Journal of Intelligent Manufacturing</i>.
    2022. doi:<a href="https://doi.org/10.1007/s10845-022-02069-x">10.1007/s10845-022-02069-x</a>
  apa: Grumbach, F., Müller, A., Reusch, P., &#38; Trojahn, S. (2022). Robust-stable
    scheduling in dynamic flow shops based on deep reinforcement learning. <i>Journal
    of Intelligent Manufacturing</i>. <a href="https://doi.org/10.1007/s10845-022-02069-x">https://doi.org/10.1007/s10845-022-02069-x</a>
  bibtex: '@article{Grumbach_Müller_Reusch_Trojahn_2022, title={Robust-stable scheduling
    in dynamic flow shops based on deep reinforcement learning}, DOI={<a href="https://doi.org/10.1007/s10845-022-02069-x">10.1007/s10845-022-02069-x</a>},
    journal={Journal of Intelligent Manufacturing}, publisher={Springer Science and
    Business Media LLC}, author={Grumbach, Felix and Müller, Anna and Reusch, Pascal
    and Trojahn, Sebastian}, year={2022} }'
  chicago: Grumbach, Felix, Anna Müller, Pascal Reusch, and Sebastian Trojahn. “Robust-Stable
    Scheduling in Dynamic Flow Shops Based on Deep Reinforcement Learning.” <i>Journal
    of Intelligent Manufacturing</i>, 2022. <a href="https://doi.org/10.1007/s10845-022-02069-x">https://doi.org/10.1007/s10845-022-02069-x</a>.
  ieee: F. Grumbach, A. Müller, P. Reusch, and S. Trojahn, “Robust-stable scheduling
    in dynamic flow shops based on deep reinforcement learning,” <i>Journal of Intelligent
    Manufacturing</i>, 2022.
  mla: Grumbach, Felix, et al. “Robust-Stable Scheduling in Dynamic Flow Shops Based
    on Deep Reinforcement Learning.” <i>Journal of Intelligent Manufacturing</i>,
    Springer Science and Business Media LLC, 2022, doi:<a href="https://doi.org/10.1007/s10845-022-02069-x">10.1007/s10845-022-02069-x</a>.
  short: F. Grumbach, A. Müller, P. Reusch, S. Trojahn, Journal of Intelligent Manufacturing
    (2022).
date_created: 2023-01-10T13:08:37Z
date_updated: 2026-06-25T11:42:46Z
doi: 10.1007/s10845-022-02069-x
jel:
- C6
keyword:
- Dynamic flow shop
- Predictive scheduling
- Proactive scheduling
- Robust scheduling
- Reinforcement learning
- Simheuristics
language:
- iso: eng
main_file_link:
- open_access: '1'
  url: https://link.springer.com/article/10.1007/s10845-022-02069-x
oa: '1'
publication: Journal of Intelligent Manufacturing
publication_identifier:
  eissn:
  - 1572-8145
  issn:
  - 0956-5515
publication_status: published
publisher: Springer Science and Business Media LLC
quality_controlled: '1'
status: public
title: Robust-stable scheduling in dynamic flow shops based on deep reinforcement
  learning
tmp:
  image: /images/cc_by.png
  legal_code_url: https://creativecommons.org/licenses/by/4.0/legalcode
  name: Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)
  short: CC BY (4.0)
type: journal_article
user_id: '245729'
year: '2022'
...