Papers › Multi-view Disentanglement for Reinforcement Learning with Multiple Cameras

Multi-view Disentanglement for Reinforcement Learning with Multiple Cameras

22 Apr 2024arXiv:2404.14064archive 2025-07-28

Mhairi Dunion, Stefano V. Albrecht

The performance of image-based Reinforcement Learning (RL) agents can vary depending on the position of the camera used to capture the images. Training on multiple cameras simultaneously, including a first-person egocentric camera, can leverage information from different camera perspectives to improve the performance of RL. However, hardware constraints may limit the availability of multiple cameras in real-world deployment. Additionally, cameras may become damaged in the real-world preventing access to all cameras that were used during training. To overcome these hardware constraints, we propose Multi-View Disentanglement (MVD), which uses multiple cameras to learn a policy that is robust to a reduction in the number of cameras to generalise to any single camera from the training set. Our approach is a self-supervised auxiliary task for RL that learns a disentangled representation from multiple cameras, with a shared representation that is aligned across all cameras to allow generalisation to a single camera, and a private representation that is camera-specific. We show experimentally that an RL agent trained on a single third-person camera is unable to learn an optimal policy in many control tasks; but, our approach, benefiting from multiple cameras during training, is able to solve the task using only the same single third-person camera.

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cosine_distance uoe-agents/mvd/algorithms/info_nce.py official repository ran fingerprinted MIT (permissive) · b5e5ec5ae2bf2ea6 · report
info_nce uoe-agents/mvd/algorithms/info_nce.py official repository ran MIT (permissive) · c105e24c6a7befa4 · report
mlp uoe-agents/mvd/utils.py official repository ran · our draft was wrong MIT (permissive) · bdef24507a4ccfa7 · report
preprocess_obs uoe-agents/mvd/utils.py official repository ran · fixture could not drive it fingerprinted MIT (permissive) · 975f23eeea267e55 · report
rect_prism_tolerance uoe-agents/mvd/metaworld/envs/reward_utils.py official repository ran fingerprinted MIT (permissive) · 083eee6f7eed5a7d · report
to_np uoe-agents/mvd/utils.py official repository ran · honoured contract MIT (permissive) · 6ee8c9901ab984a9 · report
tolerance uoe-agents/mvd/metaworld/envs/reward_utils.py official repository ran MIT (permissive) · 1ab84d50eed1d70e · report
transpose uoe-agents/mvd/algorithms/info_nce.py official repository ran · violated contract fingerprinted MIT (permissive) · 55f1419d4d8c483c · report
full_v1_path_for uoe-agents/mvd/metaworld/envs/asset_path_utils.py official repository unverified MIT (permissive) · e13df24e0d039aa9 · report
full_v2_path_for uoe-agents/mvd/metaworld/envs/asset_path_utils.py official repository unverified MIT (permissive) · 061fee9f0adfe735 · report
inverse_tolerance uoe-agents/mvd/metaworld/envs/reward_utils.py official repository unverified MIT (permissive) · e1dac90f131811f7 · report

Tasks

DisentanglementReinforcement LearningReinforcement Learning (RL)reinforcement-learning

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