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Tom Bewley
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Conference and Workshop Papers
- 2022
- [c5]Tom Bewley, Freddy Lécué:
Interpretable Preference-based Reinforcement Learning with Tree-Structured Reward Functions. AAMAS 2022: 118-126 - [c4]Joseph Early, Tom Bewley, Christine Evers, Sarvapali D. Ramchurn:
Non-Markovian Reward Modelling from Trajectory Labels via Interpretable Multiple Instance Learning. NeurIPS 2022 - 2021
- [c3]Tom Bewley, Jonathan Lawry:
TripleTree: A Versatile Interpretable Representation of Black Box Agents and their Environments. AAAI 2021: 11415-11422 - 2020
- [c2]Tom Bewley
, Jonathan Lawry
, Arthur Richards
:
Modelling Agent Policies with Interpretable Imitation Learning. TAILOR 2020: 180-186 - 2019
- [c1]Tom Bewley
, Minas V. Liarokapis
:
On The Combination of Gamification and Crowd Computation in Industrial Automation and Robotics Applications. ICRA 2019: 1955-1961
Informal and Other Publications
- 2024
- [i10]Tom Bewley, Salim I. Amoukou, Saumitra Mishra, Daniele Magazzeni, Manuela Veloso:
Counterfactual Metarules for Local and Global Recourse. CoRR abs/2405.18875 (2024) - 2023
- [i9]Tom Bewley, Jonathan Lawry, Arthur Richards:
Learning Interpretable Models of Aircraft Handling Behaviour by Reinforcement Learning from Human Feedback. CoRR abs/2305.16924 (2023) - [i8]Scott R. Jeen, Tom Bewley
, Jonathan M. Cullen:
Conservative World Models. CoRR abs/2309.15178 (2023) - 2022
- [i7]Tom Bewley, Jonathan Lawry, Arthur Richards:
Summarising and Comparing Agent Dynamics with Contrastive Spatiotemporal Abstraction. CoRR abs/2201.07749 (2022) - [i6]Joseph Early, Tom Bewley, Christine Evers
, Sarvapali D. Ramchurn
:
Non-Markovian Reward Modelling from Trajectory Labels via Interpretable Multiple Instance Learning. CoRR abs/2205.15367 (2022) - [i5]Tom Bewley, Jonathan Lawry, Arthur Richards, Rachel Craddock, Ian Henderson:
Reward Learning with Trees: Methods and Evaluation. CoRR abs/2210.01007 (2022) - 2021
- [i4]Tom Bewley, Freddy Lécué:
Interpretable Preference-based Reinforcement Learning with Tree-Structured Reward Functions. CoRR abs/2112.11230 (2021) - 2020
- [i3]Tom Bewley, Jonathan Lawry, Arthur Richards:
Modelling Agent Policies with Interpretable Imitation Learning. CoRR abs/2006.11309 (2020) - [i2]Tom Bewley:
Am I Building a White Box Agent or Interpreting a Black Box Agent? CoRR abs/2007.01187 (2020) - [i1]Tom Bewley
, Jonathan Lawry:
TripleTree: A Versatile Interpretable Representation of Black Box Agents and their Environments. CoRR abs/2009.04743 (2020)
Coauthor Index
![](https://dblp.dagstuhl.de/img/cog.dark.24x24.png)
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