Publications

* First authorships are highlighted in green.

2024

    1. Macfarlane, M., Toledo, E., Byrne, D. J., Duckworth, P., & Laterre, A. (2024). SPO: Sequential Monte Carlo Policy Optimisation. NeurIPS 2024.
    2. Toledo, E., Macfarlane, M., Byrne, D. J., Singh, S., Duckworth, P., & Laterre, A. (2024). SMX: Sequential Monte Carlo Planning for Expert Iteration. ICML 2024 Workshop: Foundations of Reinforcement Learning and Control–Connections and Perspectives.

2023

    1. Bonnet, C., Luo, D., Byrne, D., Surana, S., Abramowitz, S., Duckworth, P., Coyette, V., Midgley, L. I., Tegegn, E., Kalloniatis, T., & others. (2023). Jumanji: a diverse suite of scalable reinforcement learning environments in jax. ArXiv Preprint ArXiv:2306.09884.
    2. Hayes, C. F., Rădulescu, R., Bargiacchi, E., Kallstrom, J., Macfarlane, M., Reymond, M., Verstraeten, T., Zintgraf, L. M., Dazeley, R., Heintz, F., & others. (2023). A Brief Guide to Multi-Objective Reinforcement Learning and Planning. Proceedings of the 2023 International Conference on Autonomous Agents and Multiagent Systems, 1988–1990.
    3. Mak, S., Pearce, T., Macfarlane, M., Xu, L., Ostroumov, M., & Brintrup, A. (2023). Cooperative Logistics: Can Artificial Intelligence Enable Trustworthy Cooperation at Scale? NeurIPS 2023 Computational Sustainability: Promises and Pitfalls from Theory to Deployment.

2022

    1. Hayes, C. F., Rădulescu, R., Bargiacchi, E., Källström, J., Macfarlane, M., Reymond, M., Verstraeten, T., Zintgraf, L. M., Dazeley, R., Heintz, F., & others. (2022). A practical guide to multi-objective reinforcement learning and planning. Autonomous Agents and Multi-Agent Systems, 36(1), 26.
    2. Macfarlane, M., Roijers, D. M., & van Hoof, H. (2022). Training Graph Neural Networks with Policy Gradients to Perform Tree Search. Deep Reinforcement Learning Workshop NeurIPS 2022.