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J.W. Böhmer

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  • 2024

    Multi-Robot Local Motion Planning Using Dynamic Optimization Fabrics

    Bakker, S., Knödler, L., Spahn, M., Böhmer, J. W. & Alonso-Mora, J., 2024, Proceedings of the International Symposium on Multi-Robot and Multi-Agent Systems (MRS). IEEE, p. 149-155 7 p. (2023 International Symposium on Multi-Robot and Multi-Agent Systems, MRS 2023).

    Research output: Chapter in Book/Conference proceedings/Edited volume › Conference contribution › Scientific › peer-review

    1 Citation (Scopus)
  • 2021

    FACMAC: Factored Multi-Agent Centralised Policy Gradients

    Peng, B., Rashid, T., Schroeder de Witt, C. A., Kamienny, P-A., Torr, P. H. S., Böhmer, J. W. & Whiteson, S., 2021, Advances in Neural Information Processing Systems 34 (NeurIPS 2021): NeurIPS Proceedings. Ranzato, M., Beygelzimer, A., Dauphin, Y., Liang, P. S. & Wortman Vaughan, J. (eds.). 14 p.

    Research output: Chapter in Book/Conference proceedings/Edited volume › Conference contribution › Scientific › peer-review

  • My body is a cage: the role of morphology in graph-based incompatible control

    Kurin, V., Igl, M., Rocktäschel, T., Böhmer, W. & Whiteson, S., 2021, International Conference on Learning Representations (ICLR). 20 p.

    Research output: Chapter in Book/Conference proceedings/Edited volume › Conference contribution › Scientific › peer-review

    Open Access
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    136 Downloads (Pure)

  • Randomized Entity-wise Factorization for Multi-Agent Reinforcement Learning

    Iqbal, S., Witt, C. A. S. D., Peng, B., Böhmer, W., Whiteson, S. & Sha, F., 2021, Proceedings of the 37th International Conference on Machine Learning, ICML 2020. Meila, M. & Zhang, T. (eds.). Vol. 139. p. 4596-4606 11 p.

    Research output: Chapter in Book/Conference proceedings/Edited volume › Conference contribution › Scientific › peer-review

    Open Access
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    38 Downloads (Pure)

  • UneVEn: Universal Value Exploration for Multi-Agent Reinforcement Learning

    Gupta, T., Mahajan, A., Peng, B., Böhmer, W. & Whiteson, S., 2021, Proceedings of the International Conference on Machine Learning (ICML). Meila, M. & Zhang, T. (eds.). Vol. 139. p. 3930-3941 12 p. (Proceedings of Machine Learning Research; vol. PMLR 139).

    Research output: Chapter in Book/Conference proceedings/Edited volume › Conference contribution › Scientific › peer-review

    Open Access
    File
    53 Downloads (Pure)
  • 2020

    Deep coordination graphs

    Böhmer, W., Kurin, V. & Whiteson, S., 2020, 37th International Conference on Machine Learning, ICML 2020. Daume, H. & Singh, A. (eds.). International Machine Learning Society (IMLS), p. 957-968 12 p. (37th International Conference on Machine Learning, ICML 2020; vol. PartF168147-2).

    Research output: Chapter in Book/Conference proceedings/Edited volume › Conference contribution › Scientific › peer-review

    34 Citations (Scopus)

  • Deep residual reinforcement learning

    Zhang, S., Boehmer, W. & Whiteson, S., 2020, Proceedings of the 19th International Conference on Autonomous Agents and Multiagent Systems, AAMAS 2020. An, B., El Fallah Seghrouchni, A. & Sukthankar, G. (eds.). International Foundation for Autonomous Agents and Multiagent Systems (IFAAMAS), p. 1611-1619 9 p. (Proceedings of the International Joint Conference on Autonomous Agents and Multiagent Systems, AAMAS; vol. 2020-May).

    Research output: Chapter in Book/Conference proceedings/Edited volume › Conference contribution › Scientific › peer-review

    9 Citations (Scopus)

  • Multitask Soft Option Learning

    Igl, M., Gambardella, A., He, J., Nardelli, N., Siddharth, N., Böhmer, W. & Whiteson, S., 2020, Proceedings of the 36th Conference on Uncertainty in Artificial Intelligence (UAI). Vol. 124. p. 969-978 10 p. (Proceedings of Machine Learning Research).

    Research output: Chapter in Book/Conference proceedings/Edited volume › Conference contribution › Scientific › peer-review

    Open Access
    File
    14 Downloads (Pure)

  • Optimistic Exploration even with a Pessimistic Initialisation

    Rashid, T., Peng, B., Böhmer, W. & Whiteson, S., 2020, International Conference on Learning Representations (ICLR).

    Research output: Chapter in Book/Conference proceedings/Edited volume › Conference contribution › Scientific › peer-review

  • 2019

    Exploration with Unreliable Intrinsic Reward in Multi-Agent Reinforcement Learning

    Böhmer, W., Rashid, T. & Whiteson, S., 2019, ICML em Exploration in Reinforcement Learning workshop. Vol. abs/1906.02138. (CoRR).

    Research output: Chapter in Book/Conference proceedings/Edited volume › Conference contribution › Scientific › peer-review

  • Multi-agent hierarchical reinforcement learning with dynamic termination

    Han, D., Boehmer, W., Wooldridge, M. & Rogers, A., 1 Jan 2019, 18th International Conference on Autonomous Agents and Multiagent Systems, AAMAS 2019. International Foundation for Autonomous Agents and Multiagent Systems (IFAAMAS), p. 2006-2008 3 p. (Proceedings of the International Joint Conference on Autonomous Agents and Multiagent Systems, AAMAS; vol. 4).

    Research output: Chapter in Book/Conference proceedings/Edited volume › Conference contribution › Scientific › peer-review

    5 Citations (Scopus)

  • Multi-agent Hierarchical Reinforcement Learning with Dynamic Termination

    Han, D., Böhmer, W., Wooldridge, M. & Rogers, A., 2019, PRICAI 2019: Trends in Artificial Intelligence - 16th Pacific Rim International Conference on Artificial Intelligence, Proceedings. Nayak, A. C. & Sharma, A. (eds.). Springer, Vol. 11671. p. 80-92 13 p. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); vol. 11671 LNAI).

    Research output: Chapter in Book/Conference proceedings/Edited volume › Conference contribution › Scientific › peer-review

    4 Citations (Scopus)
  • 2016

    Non-deterministic policy improvement stabilizes approximated reinforcement learning

    Böhmer, W., Guo, R. & Obermayer, K., 2016, European Workshop on Reinforcement Learning.

    Research output: Chapter in Book/Conference proceedings/Edited volume › Conference contribution › Scientific › peer-review

  • 2015

    Regression with Linear Factored Functions

    Böhmer, W. & Obermayer, K., 2015, Machine Learning and Knowledge Discovery in Databases. Springer, Vol. 9284. p. 119-134 16 p. (Lecture Notes in Computer Science).

    Research output: Chapter in Book/Conference proceedings/Edited volume › Conference contribution › Scientific › peer-review

  • 2013

    Towards Structural Generalization: Factored Approximate Planning

    Böhmer, W. & Obermayer, K., 2013, ICRA Workshop on Autonomous Learning.

    Research output: Chapter in Book/Conference proceedings/Edited volume › Conference contribution › Scientific › peer-review

  • 2011

    Regularized Sparse Kernel Slow Feature Analysis

    Böhmer, W., Grünewälder, S., Nickisch, H. & Obermayer, K., 2011, Machine Learning and Knowledge Discovery in Databases, Part I. Springer, p. 235-248 14 p.

    Research output: Chapter in Book/Conference proceedings/Edited volume › Conference contribution › Scientific › peer-review