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generalisationTrevor-McInroe
2024
Dongge Han, Trevor McInroe, Adam Jelley, Stefano V. Albrecht, Peter Bell, Amos Storkey
LLM-Personalize: Aligning LLM Planners with Human Preferences via Reinforced Self-Training for Housekeeping Robots
arXiv:2404.14285, 2024
Abstract | BibTex | arXiv | Code | Website
generalisationstate-estimation
Abstract:
Large language models (LLMs) have shown significant potential for robotics applications, particularly task planning, by harnessing their language comprehension and text generation capabilities. However, in applications such as household robotics, a critical gap remains in the personalization of these models to individual user preferences. We introduce LLM-Personalize, a novel framework with an optimization pipeline designed to personalize LLM planners for household robotics. Our LLM-Personalize framework features an LLM planner that performs iterative planning in multi-room, partially-observable household scenarios, making use of a scene graph constructed with local observations. The generated plan consists of a sequence of high-level actions which are subsequently executed by a controller. Central to our approach is the optimization pipeline, which combines imitation learning and iterative self-training to personalize the LLM planner. In particular, the imitation learning phase performs initial LLM alignment from demonstrations, and bootstraps the model to facilitate effective iterative self-training, which further explores and aligns the model to user preferences. We evaluate LLM-Personalize on Housekeep, a challenging simulated real-world 3D benchmark for household rearrangements, and show that LLM-Personalize achieves more than a 30 percent increase in success rate over existing LLM planners, showcasing significantly improved alignment with human preferences.
@misc{han2024llmpersonalize,
title={LLM-Personalize: Aligning LLM Planners with Human Preferences via Reinforced Self-Training for Housekeeping Robots},
author={Dongge Han and Trevor McInroe and Adam Jelley and Stefano V. Albrecht and Peter Bell and Amos Storkey},
year={2024},
eprint={2404.14285},
archivePrefix={arXiv},
primaryClass={cs.RO}
}
2023
Mhairi Dunion, Trevor McInroe, Kevin Sebastian Luck, Josiah Hanna, Stefano V. Albrecht
Conditional Mutual Information for Disentangled Representations in Reinforcement Learning
Conference on Neural Information Processing Systems, 2023
Abstract | BibTex | arXiv | Code
NeurIPSdeep-rlcausalgeneralisation
Abstract:
Reinforcement Learning (RL) environments can produce training data with spurious correlations between features due to the amount of training data or its limited feature coverage. This can lead to RL agents encoding these misleading correlations in their latent representation, preventing the agent from generalising if the correlation changes within the environment or when deployed in the real world. Disentangled representations can improve robustness, but existing disentanglement techniques that minimise mutual information between features require independent features, thus they cannot disentangle correlated features. We propose an auxiliary task for RL algorithms that learns a disentangled representation of high-dimensional observations with correlated features by minimising the conditional mutual information between features in the representation. We demonstrate experimentally, using continuous control tasks, that our approach improves generalisation under correlation shifts, as well as improving the training performance of RL algorithms in the presence of correlated features.
@inproceedings{dunion2023cmid,
title={Conditional Mutual Information for Disentangled Representations in Reinforcement Learning},
author={Mhairi Dunion and Trevor McInroe and Kevin Sebastian Luck and Josiah Hanna and Stefano V. Albrecht},
booktitle={Conference on Neural Information Processing Systems},
year={2023}
}
Mhairi Dunion, Trevor McInroe, Kevin Sebastian Luck, Josiah Hanna, Stefano V. Albrecht
Temporal Disentanglement of Representations for Improved Generalisation in Reinforcement Learning
International Conference on Learning Representations, 2023
Abstract | BibTex | arXiv | Code
ICLRdeep-rlgeneralisationcausal
Abstract:
Reinforcement Learning (RL) agents are often unable to generalise well to environment variations in the state space that were not observed during training. This issue is especially problematic for image-based RL, where a change in just one variable, such as the background colour, can change many pixels in the image, which can lead to drastic changes in the agent's latent representation of the image, causing the learned policy to fail. To learn more robust representations, we introduce TEmporal Disentanglement (TED), a self-supervised auxiliary task that leads to disentangled image representations exploiting the sequential nature of RL observations. We find empirically that RL algorithms utilising TED as an auxiliary task adapt more quickly to changes in environment variables with continued training compared to state-of-the-art representation learning methods. Since TED enforces a disentangled structure of the representation, we also find that policies trained with TED generalise better to unseen values of variables irrelevant to the task (e.g. background colour) as well as unseen values of variables that affect the optimal policy (e.g. goal positions).
@inproceedings{dunion2023ted,
title={Temporal Disentanglement of Representations for Improved Generalisation in Reinforcement Learning},
author={Mhairi Dunion and Trevor McInroe and Kevin Sebastian Luck and Josiah Hanna and Stefano V. Albrecht},
booktitle={International Conference on Learning Representations (ICLR)},
year={2023}
}
Mhairi Dunion, Trevor McInroe, Kevin Sebastian Luck, Josiah Hanna, Stefano V. Albrecht
Conditional Mutual Information for Disentangled Representations in Reinforcement Learning
European Workshop on Reinforcement Learning, 2023
Abstract | BibTex | arXiv | Code
EWRLdeep-rlcausalgeneralisation
Abstract:
Reinforcement Learning (RL) environments can produce training data with spurious correlations between features due to the amount of training data or its limited feature coverage. This can lead to RL agents encoding these misleading correlations in their latent representation, preventing the agent from generalising if the correlation changes within the environment or when deployed in the real world. Disentangled representations can improve robustness, but existing disentanglement techniques that minimise mutual information between features require independent features, thus they cannot disentangle correlated features. We propose an auxiliary task for RL algorithms that learns a disentangled representation of high-dimensional observations with correlated features by minimising the conditional mutual information between features in the representation. We demonstrate experimentally, using continuous control tasks, that our approach improves generalisation under correlation shifts, as well as improving the training performance of RL algorithms in the presence of correlated features.
@inproceedings{dunion2023cmid,
title={Conditional Mutual Information for Disentangled Representations in Reinforcement Learning},
author={Mhairi Dunion and Trevor McInroe and Kevin Sebastian Luck and Josiah Hanna and Stefano V. Albrecht},
booktitle={European Workshop on Reinforcement Learning},
year={2023}
}
2022
Trevor McInroe, Lukas Schäfer, Stefano V. Albrecht
Learning Representations for Reinforcement Learning with Hierarchical Forward Models
NeurIPS Workshop on Deep Reinforcement Learning, 2022
Abstract | BibTex | arXiv
NeurIPSdeep-rlgeneralisation
Abstract:
Learning control from pixels is difficult for reinforcement learning (RL) agents because representation learning and policy learning are intertwined. Previous approaches remedy this issue with auxiliary representation learning tasks, but they either do not consider the temporal aspect of the problem or only consider single-step transitions, which may miss relevant information if important environmental changes take many steps to manifest. We propose Hierarchical k-Step Latent (HKSL), an auxiliary task that learns representations via a hierarchy of forward models that operate at varying magnitudes of step skipping while also learning to communicate between levels in the hierarchy. We evaluate HKSL in a suite of 30 robotic control tasks with and without distractors and a task of our creation. We find that HKSL either converges to higher or optimal episodic returns more quickly than several alternative representation learning approaches. Furthermore, we find that HKSL's representations capture task-relevant details accurately across timescales (even in the presence of distractors) and that communication channels between hierarchy levels organize information based on both sides of the communication process, both of which improve sample efficiency.
@inproceedings{mcinroe2022hksl,
title={Learning Representations for Reinforcement Learning with Hierarchical Forward Models},
author={Trevor McInroe and Lukas Schäfer and Stefano V. Albrecht},
booktitle={NeurIPS Workshop on Deep RL},
year={2022}
}
Mhairi Dunion, Trevor McInroe, Kevin Sebastian Luck, Josiah Hanna, Stefano V. Albrecht
Temporal Disentanglement of Representations for Improved Generalisation in Reinforcement Learning
NeurIPS Workshop on Deep Reinforcement Learning, 2022
Abstract | BibTex | arXiv | Code
NeurIPSdeep-rlgeneralisationcausal
Abstract:
Reinforcement Learning (RL) agents are often unable to generalise well to environment variations in the state space that were not observed during training. This issue is especially problematic for image-based RL, where a change in just one variable, such as the background colour, can change many pixels in the image, which can lead to drastic changes in the agent's latent representation of the image, causing the learned policy to fail. To learn more robust representations, we introduce TEmporal Disentanglement (TED), a self-supervised auxiliary task that leads to disentangled image representations exploiting the sequential nature of RL observations. We find empirically that RL algorithms utilising TED as an auxiliary task adapt more quickly to changes in environment variables with continued training compared to state-of-the-art representation learning methods. Since TED enforces a disentangled structure of the representation, we also find that policies trained with TED generalise better to unseen values of variables irrelevant to the task (e.g. background colour) as well as unseen values of variables that affect the optimal policy (e.g. goal positions).
@inproceedings{dunion2022ted,
title={Temporal Disentanglement of Representations for Improved Generalisation in Reinforcement Learning},
author={Mhairi Dunion and Trevor McInroe and Kevin Sebastian Luck and Josiah Hanna and Stefano V. Albrecht},
booktitle={NeurIPS Workshop on Deep Reinforcement Learning},
year={2022}
}