Pith. sign in

REVIEW 3 major objections 6 minor 108 references

A Survey of Reinforcement Learning for Optimization in Automation

T0 review · 3 major / 6 minor · reviewed 2026-08-07 · deepseek-v4-flash

Challenge this review Re-run · record.json Download PDF Read on arXiv ↗

Pith's one-line read This survey argues that reinforcement learning has become a practical tool for optimization across manufacturing, energy systems, and robotics, and that it provides the first structured map of the field, organizing representative studies…

desk verdict A useful organizing survey for newcomers, but Table II misassigns four HVAC papers to renewable energy integration, so the core categorization needs a fix before it can be trusted. read the letter →

arxiv 2502.09417 v1 pith:JWR6VYHB submitted 2025-02-13 cs.LG cs.AIcs.NEcs.ROcs.SYeess.SY

Ahmad Farooq , Kamran Iqbal This is my paper · ORCID
classification cs.LGcs.AIcs.NEcs.ROcs.SYeess.SY
keywords reinforcementlearningautomationmanufacturingenergysystemsroboticssampleefficiencysafetyinterpretability
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

The paper argues that reinforcement learning has become a practical tool for optimization problems in automation and that the field has matured enough to be surveyed. It sets out to organize the literature into three application domains—manufacturing, energy systems, and robotics—and into five cross-cutting challenge areas: sample efficiency and scalability, safety and robustness, interpretability and trustworthiness, transfer learning and meta-learning, and real-world deployment and integration. The authors claim this is the first survey devoted specifically to RL for optimization in automation, and they provide comparative tables that classify representative studies by objectives, RL methods, outcomes, and future directions. A sympathetic reader would take the paper's contribution to be a structured map of the field that helps researchers locate relevant work and open problems.

What carries the argument

The central organizing device is a set of comparison tables (Tables I–IV) that act as a taxonomy. Each table rows subdomains against feature columns—key objectives, challenges addressed, RL approaches, methodology highlights, outcomes, future directions, and representative studies. Table IV adds a cross-cutting view of five challenge areas, pairing each with its state of the art and future directions. These tables carry the argument: they are how the survey converts scattered individual papers into a structured claim about the field's state and needs.

What would settle it

A reader could test the paper's comprehensiveness by running a systematic literature search for RL optimization studies in each of the twelve subdomains over the same period, using explicit inclusion criteria, and checking whether the survey's cited references cover, say, the majority of qualifying papers. If large numbers of qualifying papers are missing, the survey's claim to be a comprehensive guide would be refuted; if its cites dominate the qualifying set, the claim stands.

Watch

Extended reading notes

Core claim

On the authors' own terms, the discovery is that RL-based optimization in automation has reached a state where it can be systematically categorized, and that the field's progress clusters around identifiable methods (DQN, PPO, SAC, DDPG, MARL variants) applied to recurring problem types (scheduling, inventory, maintenance, process control, demand response, microgrid management, renewable integration, HVAC control, motion planning, manipulation, multi-robot coordination, human-robot collaboration). The survey's central claim is that this categorization is the first of its kind specifically for RL in automation, that it reveals common methodological patterns across domains, and that five shared challenges—sample efficiency, safety, interpretability, transfer, deployment—currently limit real-world use. It also claims to identify the state of the art within each subdomain and to list future research directions that follow from the gaps it finds.

Load-bearing premise

The survey assumes that the handful of 'representative studies' it selects for each subdomain accurately reflects the state of the art, without describing a systematic search strategy or inclusion criteria.

Editorial extensions

If this is right

  • Researchers entering any of the twelve subdomains can use the tables to find representative baseline works and standard RL algorithms without a separate literature search.
  • The consistent pattern of challenges across domains implies that advances in sample efficiency, safety, or interpretability in one domain should transfer in method to the others.
  • The gap between simulated successes and real-world deployment, identified as a cross-cutting challenge, suggests that future work on sim-to-real transfer and human-in-the-loop integration would have broad impact.
  • The survey's categorization implies that multi-agent RL, currently prominent in inventory, microgrid, and multi-robot settings, is a general template for distributed automation optimization.
  • The identified future directions (risk-sensitive formulations, curriculum learning, explainable RL, meta-RL) provide a shared research agenda for the field.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • The survey's definition of 'optimization' is broad enough to cover almost any RL application in these domains, so its claim of being the first such survey depends on how tightly 'optimization' is scoped; a narrower reading might find earlier focused reviews.
  • The emphasis on representative studies rather than an exhaustive census suggests that the survey's tables are best read as a starting point, not a complete map; a systematic, reproducible search with inclusion criteria could extend or correct the coverage.
  • Since the paper points to transfer learning and meta-learning as future directions, one testable extension is to benchmark whether cross-domain RL policies trained in manufacturing scheduling can transfer to energy dispatch or robot planning tasks with minimal fine-tuning.
  • The repeated mention of safety and trust implies that adoption of RL in automation will depend on progress in formal verification, not just algorithmic performance; a survey tracking verification methods alongside RL would be a natural sequel.
Share X Bluesky LinkedIn Reddit HN

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

3 major / 6 minor

Summary. The paper is a survey of reinforcement learning (RL) applied to optimization problems in automation, organized around three application domains: manufacturing, energy systems, and robotics. It provides comparative tables (Tables I–III) that categorize representative studies by sub-domain, RL approach, methodology, outcomes, and future directions. Section III discusses five cross-cutting challenges—sample efficiency and scalability; safety and robustness; interpretability and trustworthiness; transfer learning and meta-learning; and real-world deployment and integration—and Section IV offers concluding remarks. The authors claim this is the first survey specifically focused on RL for optimization in automation and that it provides a systematic categorization and comprehensive bibliography.

Significance. If the categorization and claims are accurate, the survey would serve as a useful entry point for researchers and practitioners seeking a structured overview of RL applications in manufacturing, energy, and robotics. Its strengths include broad coverage of the literature, clear taxonomies in Tables I–III, and a substantial reference list spanning both application domains and cross-cutting challenges. However, the value of a survey of this kind rests on the reliability of its categorizations and quantitative statements; for that reason, the internal inconsistency in Table II is a significant obstacle to the paper's central contribution. The paper does not provide a reproducible methodology, but as a survey it can be remedied by correcting the tables and adding a short methodology statement.

major comments (3)
  1. [Section II.B, Table II] The 'Renewable Energy Integration' column lists PPO [29], Batch Constrained Munchausen Deep Q-learning [47], Q-learning [48], and A3C [45] as its RL approaches, but all four references are HVAC-control papers: [29] is whole-building HVAC control and demand response, [47] is safe HVAC control via batch RL, [48] is HVAC operation optimization, and [45] is end-to-end DRL for HVAC in office buildings. The narrative text for renewable energy integration instead cites [4], [31], [41]–[44]. This is a verifiable internal contradiction: the table assigns works to a sub-domain that contradicts the paper's own prose. Because Section II's tables are the main deliverable of the survey, this error calls the reliability of the 'systematic categorization' into question until a full audit is performed.
  2. [Section I.B] The paper claims a 'comprehensive bibliography' and repeatedly refers to 'representative studies' without stating any systematic search strategy, inclusion criteria, or quality-assessment procedure. This makes the representativeness and comprehensiveness of the selected literature unverifiable. The authors should either add a methodology subsection describing how the literature was collected and filtered, or temper the claims of comprehensiveness and of being the 'first survey' to match the actual selection process.
  3. [Section II.B, Demand Response paragraph] The statement that demand-response strategies achieve 'up to 22% energy savings' is presented without attribution to a specific experiment or study among the cited references [29]–[34]. In a survey, quantitative performance claims must be traceable to the original source so readers can assess the context, methodology, and generality of the result. Please cite the specific reference(s) and describe the conditions under which this figure was obtained.
minor comments (6)
  1. [Section I.A] The phrase 'a effective framework' should be 'an effective framework'.
  2. [Section II.B and Table II] The term 'HV AC' should be consistently written as 'HVAC' without a space.
  3. [Table IV, Safety and Robustness row] Reference [84] is by Zanon and Gros, not by Li et al.; the author attribution in the 'Related Studies' column should be corrected.
  4. [Table III, Motion Planning row] The RL Approaches column lists 'EfficientLPT [52]', which is not an RL algorithm itself but a method that uses prior policy guidance; consider moving it to the methodology highlights or clarifying its role.
  5. [Table IV, Sample Efficiency and Scalability row] The RL Approaches column lists PPO, SAC, MBPO, Dreamer, IMPALA, and Acme without citations; adding references for these algorithms would improve the completeness of the table.
  6. [Title footnote] The footnote states both that 'This work is not supported by any organization' and that the work is a preprint of a paper published at IEEE CASE 2024; please clarify the relationship between the two versions and any copyright or overlap considerations.

Circularity Check

0 steps flagged · score 0.0 of 10

No circularity; the survey only organizes external cited work, and the Table II inconsistency is an accuracy issue, not a derivation-from-inputs issue.

full rationale

This paper is a survey, not a derivation chain: it reports and categorizes results from external cited works, and it contains no fitted parameters, predictive equations, or mathematical claims whose outputs are defined by their inputs. The stated contribution—being the first survey focused on RL for optimization in automation—is a literature claim, not a consequence of the surveyed material, and no load-bearing step is justified by a self-citation (the authors' own prior work does not appear in the reference list). The skeptic's concern about Table II, where the 'Renewable Energy Integration' column lists HVAC-control papers such as Azuatalam et al. [29], Liu et al. [47], Yuan et al. [48], and Zhong et al. [45], is a real internal-consistency / categorization error that affects the survey's reliability, but it is not circularity: the survey does not use those references to define or predict anything about renewable energy integration. Likewise, the absence of a documented search strategy weakens the claim of comprehensiveness without making the claim equivalent to its inputs. Under the hard rules, no circular step can be exhibited, so the appropriate score is 0.

Assumptions & free parameters 0 free parameters · 1 assumptions · 0 invented entities

The survey introduces no mathematical claims, free parameters, or new entities; its only implicit assumption is the representativeness of the selected literature.

assumptions (1)
  • domain assumption The selected papers are representative of the state of the art in each covered subdomain.
    The survey claims comprehensiveness but provides no systematic search or inclusion criteria; see Section I.B and Section II.

how reviews work

0 comments
Cite this review

Pith. "Pith review of A Survey of Reinforcement Learning for Optimization in Automation." pith.science (2026). https://pith.science/paper/JWR6VYHB

@misc{pith2026250209417,
  author       = {Pith},
  title        = {Pith review of: A Survey of Reinforcement Learning for Optimization in Automation},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/JWR6VYHB}},
  note         = {Machine review of arXiv:2502.09417}
}
read the original abstract

Reinforcement Learning (RL) has become a critical tool for optimization challenges within automation, leading to significant advancements in several areas. This review article examines the current landscape of RL within automation, with a particular focus on its roles in manufacturing, energy systems, and robotics. It discusses state-of-the-art methods, major challenges, and upcoming avenues of research within each sector, highlighting RL's capacity to solve intricate optimization challenges. The paper reviews the advantages and constraints of RL-driven optimization methods in automation. It points out prevalent challenges encountered in RL optimization, including issues related to sample efficiency and scalability; safety and robustness; interpretability and trustworthiness; transfer learning and meta-learning; and real-world deployment and integration. It further explores prospective strategies and future research pathways to navigate these challenges. Additionally, the survey includes a comprehensive list of relevant research papers, making it an indispensable guide for scholars and practitioners keen on exploring this domain.

Figures

Figures reproduced from arXiv: 2502.09417 by the authors.

Figure 1
Figure 1. Taxonomy of Application Domains of RL for Optimization in Automation [PITH_FULL_IMAGE:figures/full_fig_p002_1.png] view at source ↗

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

108 extracted references · 69 canonical work pages

  1. [29]

    Reinforcement learning for whole-building hvac control and demand response,

    D. Azuatalam, W.-L. Lee, F. de Nijs, and A. Liebman, “Reinforcement learning for whole-building hvac control and demand response,” Energy and AI , vol. 2, p. 100020, 2020

    2020

  2. [47]

    Safe hvac control via batch reinforcement learning,

    H.-Y. Liu, B. Balaji, S. Gao, R. Gupta, and D. Hong, “Safe hvac control via batch reinforcement learning,” in 2022 ACM/IEEE 13th Interna- tional Conference on Cyber-Physical Systems (ICCPS). IEEE, 2022, pp. 181–192

    work page 2022

  3. [48]

    Study on the application of reinforcement learning in the operation optimization of hvac system,

    X. Yuan, Y. Pan, J. Yang, W. Wang, and Z. Huang, “Study on the application of reinforcement learning in the operation optimization of hvac system,” in Building Simulation , vol. 14. Springer, 2021, pp. 75–87

    work page 2021

  4. [45]

    End-to-end deep reinforcement learning control for hvac systems in office buildings,

    X. Zhong, Z. Zhang, R. Zhang, and C. Zhang, “End-to-end deep reinforcement learning control for hvac systems in office buildings,” Designs, vol. 6, no. 3, p. 52, 2022

    work page 2022

  5. [4]

    Applications of reinforcement learning in energy systems,

    A. Perera and P. Kamalaruban, “Applications of reinforcement learning in energy systems,”Renewable and Sustainable Energy Reviews , vol. 137, p. 110618, 2021

    2021

  6. [31]

    Multiagent reinforce- ment learning for energy management in residential buildings,

    M. Ahrarinouri, M. Rastegar, and A. R. Seifi, “Multiagent reinforce- ment learning for energy management in residential buildings,” IEEE Transactions on Industrial Informatics , vol. 17, no. 1, pp. 659–666, 2020

    2020

  7. [41]

    Reinforcement learning in sustainable energy and electric systems: A survey,

    T. Yang, L. Zhao, W. Li, and A. Y. Zomaya, “Reinforcement learning in sustainable energy and electric systems: A survey,”Annual Reviews in Control, vol. 49, pp. 145–163, 2020

    work page 2020

  8. [44]

    A systematic study on reinforcement learning based applications,

    K. Sivamayil, E. Rajasekar, B. Aljafari, S. Nikolovski, S. Vairavasun- daram, and I. Vairavasundaram, “A systematic study on reinforcement learning based applications,” Energies, vol. 16, no. 3, p. 1512, 2023

    2023

  9. [34]

    Testbed implementation of reinforcement learning-based demand response energy management system,

    X. Zhang, R. Lu, J. Jiang, S. H. Hong, and W. S. Song, “Testbed implementation of reinforcement learning-based demand response energy management system,” Applied energy , vol. 297, p. 117131, 2021

    work page 2021

Show all 108 references
  1. [1]

    R. S. Sutton and A. G. Barto,Reinforcement learning: An introduction. MIT press, 2018

    2018

  2. [2]

    Human-level control through deep reinforcement learning,

    V. Mnih, K. Kavukcuoglu, D. Silver, A. A. Rusu, J. Veness, M. G. Bellemare, A. Graves, M. Riedmiller, A. K. Fidjeland, G. Ostrovski et al. , “Human-level control through deep reinforcement learning,” nature, vol. 518, no. 7540, pp. 529–533, 2015

    2015

  3. [3]

    Deep reinforcement learning in smart manufacturing: A review and prospects,

    C. Li, P. Zheng, Y. Yin, B. Wang, and L. Wang, “Deep reinforcement learning in smart manufacturing: A review and prospects,” CIRP Journal of Manufacturing Science and Technology , vol. 40, pp. 75– 101, 2023

    2023

  4. [5]

    Reinforcement learning in robotics: A survey,

    J. Kober, J. A. Bagnell, and J. Peters, “Reinforcement learning in robotics: A survey,”The International Journal of Robotics Research , vol. 32, no. 11, pp. 1238–1274, 2013

    2013

  5. [6]

    Reinforcement learning applied to production planning and control,

    A. Esteso, D. Peidro, J. Mula, and M. D´ıaz-Madro˜nero, “Reinforcement learning applied to production planning and control,” International Journal of Production Research, vol. 61, no. 16, pp. 5772–5789, 2023

    2023

  6. [7]

    A review on reinforcement learning: Introduction and applications in industrial process control,

    R. Nian, J. Liu, and B. Huang, “A review on reinforcement learning: Introduction and applications in industrial process control,”Computers & Chemical Engineering , vol. 139, p. 106886, 2020

    2020

  7. [8]

    Deep reinforcement learning for inventory control: A roadmap,

    R. N. Boute, J. Gijsbrechts, W. Van Jaarsveld, and N. Vanvuchelen, “Deep reinforcement learning for inventory control: A roadmap,” European Journal of Operational Research , vol. 298, no. 2, pp. 401– 412, 2022

    2022

  8. [9]

    Metaheuristics in combinatorial optimization: Overview and conceptual comparison,

    C. Blum and A. Roli, “Metaheuristics in combinatorial optimization: Overview and conceptual comparison,” ACM computing surveys (CSUR), vol. 35, no. 3, pp. 268–308, 2003

    2003

  9. [10]

    Deep reinforcement learning: An overview,

    Y. Li, “Deep reinforcement learning: An overview,” arXiv preprint arXiv:1701.07274, 2017

    2017 arXiv

  10. [11]

    Deep reinforcement learning: A brief survey,

    K. Arulkumaran, M. P. Deisenroth, M. Brundage, and A. A. Bharath, “Deep reinforcement learning: A brief survey,”IEEE Signal Process- ing Magazine, vol. 34, no. 6, pp. 26–38, 2017

    2017

  11. [12]

    A deep reinforcement learning approach for chemical production scheduling,

    C. D. Hubbs, C. Li, N. V. Sahinidis, I. E. Grossmann, and J. M. Wassick, “A deep reinforcement learning approach for chemical production scheduling,”Computers & Chemical Engineering, vol. 141, p. 106982, 2020

    2020

  12. [13]

    Intelligent scheduling of discrete automated production line via deep reinforcement learning,

    D. Shi, W. Fan, Y. Xiao, T. Lin, and C. Xing, “Intelligent scheduling of discrete automated production line via deep reinforcement learning,” International journal of production research, vol. 58, no. 11, pp. 3362– 3380, 2020

    2020

  13. [14]

    A reinforcement learning method to scheduling problem of steel production process,

    F. Guo, Y. Li, A. Liu, and Z. Liu, “A reinforcement learning method to scheduling problem of steel production process,” inJournal of Physics: Conference Series, vol. 1486, no. 7. IOP Publishing, 2020, p. 072035

    2020

  14. [15]

    Distributional rein- forcement learning for scheduling of chemical production processes,

    M. Mowbray, D. Zhang, and E. A. D. R. Chanona, “Distributional rein- forcement learning for scheduling of chemical production processes,” arXiv preprint arXiv:2203.00636, 2022

    2022 arXiv

  15. [16]

    Reinforcement learning for multi-product multi-node inventory management in supply chains,

    N. N. Sultana, H. Meisheri, V. Baniwal, S. Nath, B. Ravin- dran, and H. Khadilkar, “Reinforcement learning for multi-product multi-node inventory management in supply chains,” arXiv preprint arXiv:2006.04037, 2020

    2006 arXiv

  16. [17]

    Reward shaping to improve the performance of deep reinforcement learning in perishable inventory management,

    B. J. De Moor, J. Gijsbrechts, and R. N. Boute, “Reward shaping to improve the performance of deep reinforcement learning in perishable inventory management,”European Journal of Operational Research , vol. 301, no. 2, pp. 535–545, 2022

    2022

  17. [18]

    Cooperative multi-agent reinforcement learning for inventory man- agement,

    M. Khirwar, K. S. Gurumoorthy, A. A. Jain, and S. Manchenahally, “Cooperative multi-agent reinforcement learning for inventory man- agement,” in Joint European Conference on Machine Learning and Knowledge Discovery in Databases . Springer, 2023, pp. 619–634

    2023

  18. [19]

    Marlim: Multi-agent reinforcement learning for inventory management,

    R. Leluc, E. Kadoche, A. Bertoncello, and S. Gourv ´enec, “Marlim: Multi-agent reinforcement learning for inventory management,”arXiv preprint arXiv:2308.01649, 2023

    2023 arXiv

  19. [20]

    Reinforcement and deep reinforce- ment learning-based solutions for machine maintenance planning, scheduling policies, and optimization,

    O. Ogunfowora and H. Najjaran, “Reinforcement and deep reinforce- ment learning-based solutions for machine maintenance planning, scheduling policies, and optimization,” Journal of Manufacturing Systems, vol. 70, pp. 244–263, 2023

    2023

  20. [21]

    Reinforcement learning for dynamic condition-based maintenance of a system with individually repairable components,

    N. Yousefi, S. Tsianikas, and D. W. Coit, “Reinforcement learning for dynamic condition-based maintenance of a system with individually repairable components,” Quality Engineering, vol. 32, no. 3, pp. 388– 408, 2020

    2020

  21. [22]

    Dynamic maintenance model for a repairable multi-component system using deep reinforcement learning,

    ——, “Dynamic maintenance model for a repairable multi-component system using deep reinforcement learning,” Quality Engineering , vol. 34, no. 1, pp. 16–35, 2022

    2022

  22. [23]

    Aircraft main- tenance check scheduling using reinforcement learning,

    P. Andrade, C. Silva, B. Ribeiro, and B. F. Santos, “Aircraft main- tenance check scheduling using reinforcement learning,” Aerospace, vol. 8, no. 4, p. 113, 2021

    2021

  23. [24]

    Network maintenance planning via multi-agent reinforcement learn- ing,

    J. Thomas, M. P. Hern ´andez, A. K. Parlikad, and R. Piechocki, “Network maintenance planning via multi-agent reinforcement learn- ing,” in 2021 IEEE International Conference on Systems, Man, and Cybernetics (SMC). IEEE, 2021, pp. 2289–2295

    2021

  24. [25]

    Reinforcement learning for statistical process control in manufacturing,

    Z. J. Viharos and R. Jakab, “Reinforcement learning for statistical process control in manufacturing,” Measurement, vol. 182, p. 109616, 2021

    2021

  25. [26]

    Explainable reinforcement learning in production control of job shop manufacturing system,

    A. Kuhnle, M. C. May, L. Sch ¨afer, and G. Lanza, “Explainable reinforcement learning in production control of job shop manufacturing system,”International Journal of Production Research, vol. 60, no. 19, pp. 5812–5834, 2022

    2022

  26. [27]

    Using process data to generate an optimal control policy via apprenticeship and reinforcement learning,

    M. Mowbray, R. Smith, E. A. Del Rio-Chanona, and D. Zhang, “Using process data to generate an optimal control policy via apprenticeship and reinforcement learning,”AIChE Journal, vol. 67, no. 9, p. e17306, 2021

    2021

  27. [28]

    Reinforcement learning for process control with application in semiconductor manufacturing,

    Y. Li, J. Du, and W. Jiang, “Reinforcement learning for process control with application in semiconductor manufacturing,”IISE Transactions, pp. 1–15, 2023

    2023

  28. [30]

    Using meta reinforcement learning to bridge the gap between simulation and experiment in energy demand response,

    D. Jang, L. Spangher, M. Khattar, U. Agwan, and C. Spanos, “Using meta reinforcement learning to bridge the gap between simulation and experiment in energy demand response,” inProceedings of the Twelfth ACM International Conference on Future Energy Systems , 2021, pp. 483–487

    2021

  29. [32]

    Deep reinforcement learning-based demand response for smart facilities energy management,

    R. Lu, R. Bai, Z. Luo, J. Jiang, M. Sun, and H.-T. Zhang, “Deep reinforcement learning-based demand response for smart facilities energy management,” IEEE Transactions on Industrial Electronics , vol. 69, no. 8, pp. 8554–8565, 2021

    2021

  30. [33]

    Multi-agent deep rein- forcement learning based demand response for discrete manufacturing systems energy management,

    R. Lu, Y.-C. Li, Y. Li, J. Jiang, and Y. Ding, “Multi-agent deep rein- forcement learning based demand response for discrete manufacturing systems energy management,” Applied Energy , vol. 276, p. 115473, 2020

    2020

  31. [35]

    Deep reinforcement learning for energy management in a microgrid with flexible demand,

    T. A. Nakabi and P. Toivanen, “Deep reinforcement learning for energy management in a microgrid with flexible demand,”Sustainable Energy, Grids and Networks, vol. 25, p. 100413, 2021

    2021

  32. [36]

    Energy management for microgrids using a reinforcement learning algorithm,

    R. Hu and A. Kwasinski, “Energy management for microgrids using a reinforcement learning algorithm,” in 2021 IEEE Green Energy and Smart Systems Conference (IGESSC) . IEEE, 2021, pp. 1–6

    2021

  33. [37]

    Deep reinforcement learning- based energy management strategy for a microgrid with flexible loads,

    B. Zhang, Z. Chen, and A. M. Ghias, “Deep reinforcement learning- based energy management strategy for a microgrid with flexible loads,” in 2023 International Conference on Power Energy Systems and Applications (ICoPESA). IEEE, 2023, pp. 187–191

    2023

  34. [38]

    Energy management in microgrid based on deep rein- forcement learning with expert knowledge,

    W. Zhang, H. Qiao, X. Xu, J. Chen, J. Xiao, K. Zhang, Y. Long, and Y. Zuo, “Energy management in microgrid based on deep rein- forcement learning with expert knowledge,” inInternational Workshop on Automation, Control, and Communication Engineering (IWACCE 2022), vol. 12492. S...

    2022

  35. [39]

    Weather-aware data-driven microgrid energy manage- ment using deep reinforcement learning,

    A. Shojaeighadikolaei, A. Ghasemi, A. G. Bardas, R. Ahmadi, and M. Hashemi, “Weather-aware data-driven microgrid energy manage- ment using deep reinforcement learning,” in 2021 North American Power Symposium (NAPS). IEEE, 2021, pp. 1–6

    2021

  36. [40]

    Intelligent multi-microgrid energy management based on deep neural network and model-free reinforcement learning,

    Y. Du and F. Li, “Intelligent multi-microgrid energy management based on deep neural network and model-free reinforcement learning,”IEEE Transactions on Smart Grid , vol. 11, no. 2, pp. 1066–1076, 2019

    2019

  37. [42]

    Reinforcement learning and its applications in modern power and energy systems: A review,

    D. Cao, W. Hu, J. Zhao, G. Zhang, B. Zhang, Z. Liu, Z. Chen, and F. Blaabjerg, “Reinforcement learning and its applications in modern power and energy systems: A review,” Journal of modern power systems and clean energy , vol. 8, no. 6, pp. 1029–1042, 2020

    2020

  38. [43]

    Reinforcement learning for selective key applications in power systems: Recent advances and future challenges,

    X. Chen, G. Qu, Y. Tang, S. Low, and N. Li, “Reinforcement learning for selective key applications in power systems: Recent advances and future challenges,” IEEE Transactions on Smart Grid , vol. 13, no. 4, pp. 2935–2958, 2022

    2022

  39. [46]

    A review of reinforcement learn- ing applications to control of heating, ventilation and air conditioning systems,

    S. Sierla, H. Ihasalo, and V. Vyatkin, “A review of reinforcement learn- ing applications to control of heating, ventilation and air conditioning systems,” Energies, vol. 15, no. 10, p. 3526, 2022

    2022

  40. [49]

    Experimental evalu- ation of model-free reinforcement learning algorithms for continuous hvac control,

    M. Biemann, F. Scheller, X. Liu, and L. Huang, “Experimental evalu- ation of model-free reinforcement learning algorithms for continuous hvac control,” Applied Energy, vol. 298, p. 117164, 2021

    2021

  41. [50]

    Robotic arm motion planning based on curriculum reinforcement learning,

    D. Zhou, R. Jia, and H. Yao, “Robotic arm motion planning based on curriculum reinforcement learning,” in 2021 6th International Conference on Control and Robotics Engineering (ICCRE) . IEEE, 2021, pp. 44–49

    2021

  42. [51]

    Reinforcement learning based user-guided motion planning for human-robot collaboration,

    T. Yu and Q. Chang, “Reinforcement learning based user-guided motion planning for human-robot collaboration,” arXiv preprint arXiv:2207.00492, 2022

    2022 arXiv

  43. [52]

    Reinforcement learning with prior policy guidance for motion planning of dual-arm free-floating space robot,

    Y. Cao, S. Wang, X. Zheng, W. Ma, X. Xie, and L. Liu, “Reinforcement learning with prior policy guidance for motion planning of dual-arm free-floating space robot,” Aerospace Science and Technology , vol. 136, p. 108098, 2023

    2023

  44. [53]

    Dext-gen: Dexterous grasping in sparse reward environments with full orientation control,

    M. Schuck, J. Br¨ udigam, A. Capone, S. Sosnowski, and S. Hirche, “Dext-gen: Dexterous grasping in sparse reward environments with full orientation control,” arXiv preprint arXiv:2206.13966, 2022

    2022 arXiv

  45. [54]

    Robotic grasping using deep reinforcement learning,

    S. Joshi, S. Kumra, and F. Sahin, “Robotic grasping using deep reinforcement learning,” in2020 IEEE 16th International Conference on Automation Science and Engineering (CASE) . IEEE, 2020, pp. 1461–1466

    2020

  46. [55]

    Mrcdrl: Multi-robot coordination with deep reinforcement learning,

    D. Wang, H. Deng, and Z. Pan, “Mrcdrl: Multi-robot coordination with deep reinforcement learning,” Neurocomputing, vol. 406, pp. 68–76, 2020

    2020

  47. [56]

    Towards pick and place multi robot coordination using multi-agent deep reinforcement learning,

    X. Lan, Y. Qiao, and B. Lee, “Towards pick and place multi robot coordination using multi-agent deep reinforcement learning,” in 2021 7th International Conference on Automation, Robotics and Applications (ICARA). IEEE, 2021, pp. 85–89

    2021

  48. [57]

    Human-centered collaborative robots with deep reinforcement learn- ing,

    A. Ghadirzadeh, X. Chen, W. Yin, Z. Yi, M. Bj¨orkman, and D. Kragic, “Human-centered collaborative robots with deep reinforcement learn- ing,”IEEE Robotics and Automation Letters, vol. 6, no. 2, pp. 566–571, 2020

    2020

  49. [58]

    Explainable reinforcement learning for human-robot collaboration,

    A. Iucci, A. Hata, A. Terra, R. Inam, and I. Leite, “Explainable reinforcement learning for human-robot collaboration,” in 2021 20th International Conference on Advanced Robotics (ICAR). IEEE, 2021, pp. 927–934

    2021

  50. [59]

    Real-world human-robot collaborative reinforcement learning,

    A. Shafti, J. Tjomsland, W. Dudley, and A. A. Faisal, “Real-world human-robot collaborative reinforcement learning,” in2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) . IEEE, 2020, pp. 11 161–11 166

    2020

  51. [60]

    Human-robot gym: Benchmarking reinforcement learning in human-robot collaboration,

    J. Thumm, F. Trost, and M. Althoff, “Human-robot gym: Benchmarking reinforcement learning in human-robot collaboration,” arXiv preprint arXiv:2310.06208, 2023

    2023 arXiv

  52. [61]

    Towards safe human-robot collaboration using deep reinforcement learning,

    M. El-Shamouty, X. Wu, S. Yang, M. Albus, and M. F. Huber, “Towards safe human-robot collaboration using deep reinforcement learning,” in 2020 IEEE international conference on robotics and automation (ICRA). IEEE, 2020, pp. 4899–4905

    2020

  53. [62]

    A framework and algorithm for human-robot collaboration based on multimodal reinforcement learning,

    Z. Cai, Z. Feng, L. Zhou, C. Ai, H. Shao, X. Yang et al. , “A framework and algorithm for human-robot collaboration based on multimodal reinforcement learning,” Computational Intelligence and Neuroscience, vol. 2022, 2022

    2022

  54. [63]

    A survey of learning-based robot motion planning,

    J. Wang, T. Zhang, N. Ma, Z. Li, H. Ma, F. Meng, and M. Q.-H. Meng, “A survey of learning-based robot motion planning,”IET Cyber- Systems and Robotics, vol. 3, no. 4, pp. 302–314, 2021

    2021

  55. [64]

    A survey on deep reinforcement learning algorithms for robotic manipulation,

    D. Han, B. Mulyana, V. Stankovic, and S. Cheng, “A survey on deep reinforcement learning algorithms for robotic manipulation,” Sensors, vol. 23, no. 7, p. 3762, 2023

    2023

  56. [65]

    Reward shaping to learn natural object manipulation with an anthropomorphic robotic hand and hand pose priors via on- policy reinforcement learning,

    P. Rivera, J. Oh, E. Valarezo, G. Ryu, H. Jung, J. H. Lee, J. G. Jeong, and T.-S. Kim, “Reward shaping to learn natural object manipulation with an anthropomorphic robotic hand and hand pose priors via on- policy reinforcement learning,” in 2021 International Conference on Inf...

    2021

  57. [66]

    Enhancing robotic grasping of free-floating targets with soft actor-critic algorithm and tactile sensors: a focus on the pre-grasp stage,

    B. Beigomi and Z. H. Zhu, “Enhancing robotic grasping of free-floating targets with soft actor-critic algorithm and tactile sensors: a focus on the pre-grasp stage,” in AIAA SCITECH 2024 Forum , 2024, p. 2419

    2024

  58. [67]

    Reinforcement learning for multi-robot system: A review,

    X. Yang, “Reinforcement learning for multi-robot system: A review,” in 2021 2nd International Conference on Computing and Data Science (CDS). IEEE, 2021, pp. 203–213

    2021

  59. [68]

    Coordination of a multi robot system for pick and place using reinforcement learning,

    X. Lan, Y. Qiao, and B. Lee, “Coordination of a multi robot system for pick and place using reinforcement learning,” in 2022 2nd International Conference on Computers and Automation (CompAuto). IEEE, 2022, pp. 87–92

    2022

  60. [69]

    A. K. Sadhu and A. Konar, Multi-agent coordination: A reinforcement learning approach. John Wiley & Sons, 2020

    2020

  61. [70]

    Adaptive coordination of multiple learning strategies in brains and robots,

    M. Khamassi, “Adaptive coordination of multiple learning strategies in brains and robots,” inTheory and Practice of Natural Computing: 9th International Conference, TPNC 2020, Taoyuan, Taiwan, December 7–9, 2020, Proceedings 9 . Springer, 2020, pp. 3–22

    2020

  62. [71]

    Study of sample efficiency improvements for reinforcement learning algorithms,

    T. Cao, “Study of sample efficiency improvements for reinforcement learning algorithms,” in 2020 IEEE Integrated STEM Education Conference (ISEC). IEEE, 2020, pp. 1–1

    2020

  63. [72]

    Measuring progress in deep reinforcement learning sample efficiency,

    F. E. Dorner, “Measuring progress in deep reinforcement learning sample efficiency,” arXiv preprint arXiv:2102.04881, 2021

    2021 arXiv

  64. [73]

    Maximum mutation reinforcement learning for scalable control,

    K. Suri, X. Q. Shi, K. N. Plataniotis, and Y. A. Lawryshyn, “Maximum mutation reinforcement learning for scalable control,” arXiv preprint arXiv:2007.13690, 2020

    2007 arXiv

  65. [74]

    Sample efficient reinforcement learning method via high efficient episodic memory,

    D. Yang, X. Qin, X. Xu, C. Li, and G. Wei, “Sample efficient reinforcement learning method via high efficient episodic memory,” IEEE Access, vol. 8, pp. 129 274–129 284, 2020

    2020

  66. [75]

    Efficient online reinforcement learning with offline data,

    P. J. Ball, L. Smith, I. Kostrikov, and S. Levine, “Efficient online reinforcement learning with offline data,” in International Conference on Machine Learning . PMLR, 2023, pp. 1577–1594

    2023

  67. [76]

    Breaking the sample size barrier in model-based reinforcement learning with a generative model,

    G. Li, Y. Wei, Y. Chi, Y. Gu, and Y. Chen, “Breaking the sample size barrier in model-based reinforcement learning with a generative model,” Advances in neural information processing systems , vol. 33, pp. 12 861–12 872, 2020

    2020

  68. [77]

    Elastic step ddpg: Multi-step reinforcement learning for improved sample efficiency,

    A. Ly, R. Dazeley, P. Vamplew, F. Cruz, and S. Aryal, “Elastic step ddpg: Multi-step reinforcement learning for improved sample efficiency,” in 2023 International Joint Conference on Neural Networks (IJCNN) . IEEE, 2023, pp. 01–06

    2023

  69. [78]

    Sample-efficient reinforcement learning via conservative model-based actor-critic,

    Z. Wang, J. Wang, Q. Zhou, B. Li, and H. Li, “Sample-efficient reinforcement learning via conservative model-based actor-critic,” in Proceedings of the AAAI Conference on Artificial Intelligence, vol. 36, no. 8, 2022, pp. 8612–8620

    2022

  70. [79]

    Safety robustness of reinforcement learning policies: A view from robust control,

    H. Xiong and X. Diao, “Safety robustness of reinforcement learning policies: A view from robust control,” Neurocomputing, vol. 422, pp. 12–21, 2021

    2021

  71. [80]

    Safe reinforcement learning with dual robustness,

    Z. Li, C. Hu, Y. Wang, Y. Yang, and S. E. Li, “Safe reinforcement learning with dual robustness,” arXiv preprint arXiv:2309.06835 , 2023

    2023 arXiv

  72. [81]

    On the robustness of safe reinforcement learning under observational perturbations,

    Z. Liu, Z. Guo, Z. Cen, H. Zhang, J. Tan, B. Li, and D. Zhao, “On the robustness of safe reinforcement learning under observational perturbations,” arXiv preprint arXiv:2205.14691, 2022

    2022 arXiv

  73. [82]

    Safe reinforcement learning using robust control barrier functions,

    Y. Emam, G. Notomista, P. Glotfelter, Z. Kira, and M. Egerstedt, “Safe reinforcement learning using robust control barrier functions,” IEEE Robotics and Automation Letters , 2022

    2022

  74. [83]

    Safe reinforcement learning using robust action governor,

    Y. Li, N. Li, H. E. Tseng, A. Girard, D. Filev, and I. Kolmanovsky, “Safe reinforcement learning using robust action governor,” in Learning for Dynamics and Control . PMLR, 2021, pp. 1093–1104

    2021

  75. [84]

    Safe reinforcement learning using robust mpc,

    M. Zanon and S. Gros, “Safe reinforcement learning using robust mpc,” IEEE Transactions on Automatic Control , vol. 66, no. 8, pp. 3638– 3652, 2020

    2020

  76. [85]

    Optimal transport perturbations for safe reinforcement learning with robustness guarantees,

    J. Queeney, E. C. Ozcan, I. C. Paschalidis, and C. G. Cassandras, “Optimal transport perturbations for safe reinforcement learning with robustness guarantees,” arXiv preprint arXiv:2301.13375, 2023

    2023 arXiv

  77. [86]

    Task-agnostic safety for rein- forcement learning,

    M. A. Rahman and S. Alqahtani, “Task-agnostic safety for rein- forcement learning,” in Proceedings of the 16th ACM Workshop on Artificial Intelligence and Security , 2023, pp. 139–148

    2023

  78. [87]

    Falsification-based robust ad- versarial reinforcement learning,

    X. Wang, S. Nair, and M. Althoff, “Falsification-based robust ad- versarial reinforcement learning,” in 2020 19th IEEE International Conference on Machine Learning and Applications (ICMLA). IEEE, 2020, pp. 205–212

    2020

  79. [88]

    A survey on interpretable reinforcement learning,

    C. Glanois, P. Weng, M. Zimmer, D. Li, T. Yang, J. Hao, and W. Liu, “A survey on interpretable reinforcement learning,” arXiv preprint arXiv:2112.13112, 2021

    2021 arXiv

  80. [89]

    Interpretable model-based hierarchical reinforce- ment learning using inductive logic programming,

    D. Xu and F. Fekri, “Interpretable model-based hierarchical reinforce- ment learning using inductive logic programming,” arXiv preprint arXiv:2106.11417, 2021

    2021 arXiv

  81. [90]

    There is no accuracy- interpretability tradeoff in reinforcement learning for mazes,

    Y. Mansour, M. Moshkovitz, and C. Rudin, “There is no accuracy- interpretability tradeoff in reinforcement learning for mazes,” arXiv preprint arXiv:2206.04266, 2022

    2022 arXiv

  82. [91]

    What do rein- forcement learning models measure? interpreting model parameters in cognition and neuroscience,

    M. K. Eckstein, L. Wilbrecht, and A. G. Collins, “What do rein- forcement learning models measure? interpreting model parameters in cognition and neuroscience,” Current opinion in behavioral sciences , vol. 41, pp. 128–137, 2021

    2021

  83. [92]

    Reinforcement learning interpretation methods: A survey,

    A. Alharin, T.-N. Doan, and M. Sartipi, “Reinforcement learning interpretation methods: A survey,” IEEE Access, vol. 8, pp. 171 058– 171 077, 2020

    2020

  84. [93]

    Self- supervised discovering of interpretable features for reinforcement learning,

    W. Shi, G. Huang, S. Song, Z. Wang, T. Lin, and C. Wu, “Self- supervised discovering of interpretable features for reinforcement learning,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 44, no. 5, pp. 2712–2724, 2020

    2020

  85. [94]

    Learning sparse evidence-driven interpretation to understand deep reinforcement learning agents,

    G. Dao, W. H. Huff, and M. Lee, “Learning sparse evidence-driven interpretation to understand deep reinforcement learning agents,” in 2021 IEEE Symposium Series on Computational Intelligence (SSCI) . IEEE, 2021, pp. 1–7

    2021

  86. [95]

    Meta- learning in neural networks: A survey,

    T. Hospedales, A. Antoniou, P. Micaelli, and A. Storkey, “Meta- learning in neural networks: A survey,”IEEE transactions on pattern analysis and machine intelligence, vol. 44, no. 9, pp. 5149–5169, 2021

    2021

  87. [96]

    Learning action translator for meta reinforcement learning on sparse-reward tasks,

    Y. Guo, Q. Wu, and H. Lee, “Learning action translator for meta reinforcement learning on sparse-reward tasks,” inProceedings of the AAAI Conference on Artificial Intelligence , vol. 36, no. 6, 2022, pp. 6792–6800

    2022

  88. [97]

    Curriculum learning for reinforcement learning domains: A framework and survey,

    S. Narvekar, B. Peng, M. Leonetti, J. Sinapov, M. E. Taylor, and P. Stone, “Curriculum learning for reinforcement learning domains: A framework and survey,” Journal of Machine Learning Research , vol. 21, no. 181, pp. 1–50, 2020

    2020

  89. [98]

    Effective reinforcement learning using transfer learning,

    N. S. Varma, V. Sinha et al., “Effective reinforcement learning using transfer learning,” in 2022 IEEE International Conference on Data Science and Information System (ICDSIS) . IEEE, 2022, pp. 1–6

    2022

  90. [99]

    Multi-source transfer learning for deep model-based reinforcement learning,

    R. Sasso, “Multi-source transfer learning for deep model-based reinforcement learning,” Ph.D. dissertation, 2021

    2021

  91. [100]

    Efficient meta reinforcement learning for preference-based fast adaptation,

    Z. Ren, A. Liu, Y. Liang, J. Peng, and J. Ma, “Efficient meta reinforcement learning for preference-based fast adaptation,”Advances in Neural Information Processing Systems, vol. 35, pp. 15 502–15 515, 2022

    2022

  92. [101]

    An empirical investigation of the challenges of real-world reinforcement learning,

    G. Dulac-Arnold, N. Levine, D. J. Mankowitz, J. Li, C. Paduraru, S. Gowal, and T. Hester, “An empirical investigation of the challenges of real-world reinforcement learning,”arXiv preprint arXiv:2003.11881, 2020

    2003 arXiv

  93. [102]

    Deployment-efficient reinforcement learning via model-based offline optimization,

    T. Matsushima, H. Furuta, Y. Matsuo, O. Nachum, and S. Gu, “Deployment-efficient reinforcement learning via model-based offline optimization,” arXiv preprint arXiv:2006.03647, 2020

    2006 arXiv

  94. [103]

    Deploying deep reinforcement learning systems: A taxonomy of challenges,

    A. H. Yahmed, A. A. Abbassi, A. Nikanjam, H. Li, and F. Khomh, “Deploying deep reinforcement learning systems: A taxonomy of challenges,” in 2023 IEEE International Conference on Software Maintenance and Evolution (ICSME) . IEEE, 2023, pp. 26–38

    2023

  95. [104]

    Deploying offline reinforcement learning with human feedback,

    Z. Li, K. Xu, L. Liu, L. Li, D. Ye, and P. Zhao, “Deploying offline reinforcement learning with human feedback,” arXiv preprint arXiv:2303.07046, 2023

    2023 arXiv

  96. [105]

    Evaluating the progress of deep reinforcement learning in the real world: align- ing domain-agnostic and domain-specific research,

    J. J. Garau-Luis, E. Crawley, and B. Cameron, “Evaluating the progress of deep reinforcement learning in the real world: align- ing domain-agnostic and domain-specific research,” arXiv preprint arXiv:2107.03015, 2021

    2021 arXiv

  97. [106]

    Engineering a platform for reinforcement learning workloads,

    A. Kanso and K. Patra, “Engineering a platform for reinforcement learning workloads,” in Proceedings of the 1st International Con- ference on AI Engineering: Software Engineering for AI , 2022, pp. 88–89

    2022

  98. [107]

    A comprehensive survey on safe rein- forcement learning,

    J. Garcıa and F. Fern ´andez, “A comprehensive survey on safe rein- forcement learning,” Journal of Machine Learning Research , vol. 16, no. 1, pp. 1437–1480, 2015

    2015

  99. [108]

    Safe reinforcement learning via curriculum induction,

    M. Turchetta, A. Kolobov, S. Shah, A. Krause, and A. Agarwal, “Safe reinforcement learning via curriculum induction,”Advances in Neural Information Processing Systems, vol. 33, pp. 12 151–12 162, 2020

    2020

Pith tools

Reviewed August 7, 2026 · model on record in the stance chip above.