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NeurIPScausaldeep-rl
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}
}
Guy Azran, Mohamad Hosein Danesh, Stefano V. Albrecht, Sarah Keren
Enhancing Transfer of Reinforcement Learning Agents with Abstract Contextual Embeddings
NeurIPS Workshop on Neuro Causal and Symbolic AI, 2022
Abstract | BibTex
NeurIPSdeep-rlcausal
Abstract:
Deep reinforcement learning (DRL) algorithms have seen great success in performing a plethora of tasks, but often have trouble adapting to changes in the environment. We address this issue by using reward machines (RM), a graph-based abstraction of the underlying task to represent the current setting or context. Using a graph neural network (GNN), we embed the RMs into deep latent vector representations and provide them to the agent to enhance its ability to adapt to new contexts. To the best of our knowledge, this is the first work to embed contextual abstractions and let the agent decide how to use them. Our preliminary empirical evaluation demonstrates improved sample efficiency of our approach upon context transfer on a set of grid navigation tasks.
@inproceedings{Azran2022enhancing,
title={Enhancing Transfer of Reinforcement Learning Agents with Abstract Contextual Embeddings},
author={Guy Azran and Mohamad Hosein Danesh and Stefano V Albrecht and Sarah Keren},
booktitle={NeurIPS Workshop on Neuro Causal and Symbolic AI (https://ncsi.cause-lab.net)},
year={2022}
}