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ORCIDDavid M. Bossens
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2020 – today
- 2024
[j5]David M. Bossens
, Adam J. Sobey:
Lifetime policy reuse and the importance of task capacity. AI Commun. 37(1): 115-148 (2024)- 2023
- [j4]David M. Bossens, Nicholas Bishop:
Explicit Explore, Exploit, or Escape (E4): near-optimal safety-constrained reinforcement learning in polynomial time. Mach. Learn. 112(3): 817-858 (2023) - [e1]Gabriel Pedroza, Xiaowei Huang, Xin Cynthia Chen, Andreas Theodorou, Huáscar Espinoza, Nikolaos Matragkas, José Hernández-Orallo, Mauricio Castillo-Effen, Richard Mallah, John A. McDermid, David M. Bossens, Bettina Könighofer, Sebastian Tschiatschek, Anqi Liu:
Proceedings of the IJCAI-23 Joint Workshop on Artificial Intelligence Safety and Safe Reinforcement Learning (AISafety-SafeRL 2023) co-located with the 32nd International Joint Conference on Artificial Intelligence(IJCAI2023), Macau, China, August 21-22, 2023. CEUR Workshop Proceedings 3505, CEUR-WS.org 2023 [contents] - [i11]David M. Bossens:
Robust Lagrangian and Adversarial Policy Gradient for Robust Constrained Markov Decision Processes. CoRR abs/2308.11267 (2023) - 2022
- [j3]David M. Bossens, Danesh Tarapore:
Quality-Diversity Meta-Evolution: Customizing Behavior Spaces to a Meta-Objective. IEEE Trans. Evol. Comput. 26(5): 1171-1181 (2022) - [i10]David M. Bossens, Sarvapali D. Ramchurn, Danesh Tarapore:
Resilient robot teams: a review integrating decentralised control, change-detection, and learning. CoRR abs/2204.10063 (2022) - [i9]David M. Bossens, Christine Evers:
Trust in Language Grounding: a new AI challenge for human-robot teams. CoRR abs/2209.02066 (2022) - [i8]David M. Bossens, Philip Thomas:
Low Variance Off-policy Evaluation with State-based Importance Sampling. CoRR abs/2212.03932 (2022) - 2021
- [j2]David M. Bossens, Danesh Tarapore:
QED: Using Quality-Environment-Diversity to Evolve Resilient Robot Swarms. IEEE Trans. Evol. Comput. 25(2): 346-357 (2021) - [c4]David M. Bossens, Danesh Tarapore:
On the use of feature-maps for improved quality-diversity meta-evolution. GECCO Companion 2021: 83-84 - [c3]Toby Thomas, David M. Bossens, Danesh Tarapore:
ASVLite: a high-performance simulator for autonomous surface vehicles. ICRA 2021: 2249-2255 - [c2]David M. Bossens, Danesh Tarapore:
Rapidly adapting robot swarms with Swarm Map-based Bayesian Optimisation. ICRA 2021: 9848-9854 - [i7]David M. Bossens, Danesh Tarapore:
On the use of feature-maps and parameter control for improved quality-diversity meta-evolution. CoRR abs/2105.10317 (2021) - [i6]David M. Bossens, Adam J. Sobey:
Lifetime policy reuse and the importance of task capacity. CoRR abs/2106.01741 (2021) - [i5]David M. Bossens, Danesh Tarapore:
Quality-Diversity Meta-Evolution: customising behaviour spaces to a meta-objective. CoRR abs/2109.03918 (2021) - [i4]David M. Bossens, Nicholas Bishop:
Explicit Explore, Exploit, or Escape (E4): near-optimal safety-constrained reinforcement learning in polynomial time. CoRR abs/2111.07395 (2021) - 2020
- [b1]David M. Bossens:
Reinforcement learning with limited prior knowledge in long-term environments. University of Southampton, UK, 2020 - [c1]David M. Bossens, Jean-Baptiste Mouret, Danesh Tarapore:
Learning behaviour-performance maps with meta-evolution. GECCO 2020: 49-57 - [i3]David M. Bossens, Danesh Tarapore:
QED: using Quality-Environment-Diversity to evolve resilient robot swarms. CoRR abs/2003.02341 (2020) - [i2]Toby Thomas, David M. Bossens, Danesh Tarapore:
ASV-Swarm: a high-performance simulator for the dynamics of a swarm of autonomous marine vehicles in waves. CoRR abs/2003.04599 (2020) - [i1]David M. Bossens, Danesh Tarapore:
Rapidly adapting robot swarms with Swarm Map-based Bayesian Optimisation. CoRR abs/2012.11444 (2020)
2010 – 2019
- 2019
- [j1]David M. Bossens, Nicholas C. Townsend, Adam J. Sobey:
Learning to learn with active adaptive perception. Neural Networks 115: 30-49 (2019)
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