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Scott Jeen

Possible papers associated with this exact author name in Arrow. This page groups case-insensitive exact name matches and is not a full identity disambiguation profile.

4 papers
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4

RLC Conference 2025 Conference Paper

Zero-Shot Reinforcement Learning Under Partial Observability

  • Scott Jeen
  • Tom Bewley
  • Jonathan Cullen

Recent work has shown that, under certain assumptions, zero-shot reinforcement learning (RL) methods can generalise to *any* unseen task in an environment after an offline, reward-free pre-training phase. Access to Markov states is one such assumption, yet, in many real-world applications, the Markov state is often only *partially observable*. Here, we explore how the performance of standard zero-shot RL methods degrades when subjected to partially observability, and show that, as in single-task RL, memory-based architectures are an effective remedy. We evaluate our *memory-based* zero-shot RL methods in domains where the states, rewards and a change in dynamics are partially observed, and show improved performance over memory-free baselines. Our code is open-sourced via the project page: https: //enjeeneer. io/projects/bfms-with-memory/.

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RLJ Journal 2025 Journal Article

Zero-Shot Reinforcement Learning Under Partial Observability

  • Scott Jeen
  • Tom Bewley
  • Jonathan Cullen

Recent work has shown that, under certain assumptions, zero-shot reinforcement learning (RL) methods can generalise to *any* unseen task in an environment after an offline, reward-free pre-training phase. Access to Markov states is one such assumption, yet, in many real-world applications, the Markov state is often only *partially observable*. Here, we explore how the performance of standard zero-shot RL methods degrades when subjected to partially observability, and show that, as in single-task RL, memory-based architectures are an effective remedy. We evaluate our *memory-based* zero-shot RL methods in domains where the states, rewards and a change in dynamics are partially observed, and show improved performance over memory-free baselines. Our code is open-sourced via the project page: https://enjeeneer.io/projects/bfms-with-memory/.

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NeurIPS Conference 2024 Conference Paper

Zero-Shot Reinforcement Learning from Low Quality Data

  • Scott Jeen
  • Tom Bewley
  • Jonathan M. Cullen

Zero-shot reinforcement learning (RL) promises to provide agents that can perform any task in an environment after an offline, reward-free pre-training phase. Methods leveraging successor measures and successor features have shown strong performance in this setting, but require access to large heterogenous datasets for pre-training which cannot be expected for most real problems. Here, we explore how the performance of zero-shot RL methods degrades when trained on small homogeneous datasets, and propose fixes inspired by conservatism, a well-established feature of performant single-task offline RL algorithms. We evaluate our proposals across various datasets, domains and tasks, and show that conservative zero-shot RL algorithms outperform their non-conservative counterparts on low quality datasets, and perform no worse on high quality datasets. Somewhat surprisingly, our proposals also outperform baselines that get to see the task during training. Our code is available via the project page https: //enjeeneer. io/projects/zero-shot-rl/.

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AAAI Conference 2023 Conference Paper

Low Emission Building Control with Zero-Shot Reinforcement Learning

  • Scott Jeen
  • Alessandro Abate
  • Jonathan M. Cullen

Heating and cooling systems in buildings account for 31% of global energy use, much of which are regulated by Rule Based Controllers (RBCs) that neither maximise energy efficiency nor minimise emissions by interacting optimally with the grid. Control via Reinforcement Learning (RL) has been shown to significantly improve building energy efficiency, but existing solutions require access to building-specific simulators or data that cannot be expected for every building in the world. In response, we show it is possible to obtain emission-reducing policies without such knowledge a priori–a paradigm we call zero-shot building control. We combine ideas from system identification and model-based RL to create PEARL (Probabilistic Emission-Abating Reinforcement Learning) and show that a short period of active exploration is all that is required to build a performant model. In experiments across three varied building energy simulations, we show PEARL outperforms an existing RBC once, and popular RL baselines in all cases, reducing building emissions by as much as 31% whilst maintaining thermal comfort. Our source code is available online via: https://enjeeneer.io/projects/pearl/.

PDF Details DOI
v2026.09.13