@inproceedings{zhang2023robust,

title = {Robust Reinforcement Learning in Continuous Control Tasks with Uncertainty Set Regularization},

author = {Yuan Zhang and Jianhong Wang and Joschka Boedecker},

url = {https://openreview.net/forum?id=keAPCON4jHC},

year  = {2023},

date = {2023-10-16},

urldate = {2023-10-16},

booktitle = {7th Annual Conference on Robot Learning},

abstract = {Reinforcement learning (RL) is recognized as lacking generalization and robustness under environmental perturbations, which excessively restricts its application for real-world robotics. Prior work claimed that adding regularization to the value function is equivalent to learning a robust policy under uncertain transitions. Although the regularization-robustness transformation is appealing for its simplicity and efficiency, it is still lacking in continuous control tasks. In this paper, we propose a new regularizer named Uncertainty Set Regularizer (USR), to formulate the uncertainty set on the parametric space of a transition function. To deal with unknown uncertainty sets, we further propose a novel adversarial approach to generate them based on the value function. We evaluate USR on the Real-world Reinforcement Learning (RWRL) benchmark and the Unitree A1 Robot, demonstrating improvements in the robust performance of perturbed testing environments and sim-to-real scenarios.},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{yan2023geometric,

title = {Geometric Regularity with Robot Intrinsic Symmetry in Reinforcement Learning},

author = {Schengchao Yan and Yuan Zhang and Baohe Zhang and Joschka Boedecker and Wolfram Burgard},

url = {https://doi.org/10.48550/arXiv.2306.16316},

year  = {2023},

date = {2023-06-28},

urldate = {2023-06-28},

booktitle = {RSS 2023 Workshop on Symmetries in Robot Learning},

abstract = {Geometric regularity, which leverages data symmetry, has been successfully incorporated into deep learning architectures such as CNNs, RNNs, GNNs, and Transformers. While this concept has been widely applied in robotics to address the curse of dimensionality when learning from high-dimensional data, the inherent reflectional and rotational symmetry of robot structures has not been adequately explored. Drawing inspiration from cooperative multi-agent reinforcement learning, we introduce novel network structures for deep learning algorithms that explicitly capture this geometric regularity. Moreover, we investigate the relationship between the geometric prior and the concept of Parameter Sharing in multi-agent reinforcement learning. Through experiments conducted on various challenging continuous control tasks, we demonstrate the significant potential of the proposed geometric regularity in enhancing robot learning capabilities.},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{wang2021shaq,

title = {SHAQ: Incorporating Shapley Value Theory into Multi-Agent Q-Learning},

author = {Jianhong Wang and Jinxin Wang and Yuan Zhang and Yunjie Gu and Tae-Kyun Kim},

url = {https://openreview.net/forum?id=BjGawodFnOy

https://arxiv.org/abs/2105.15013},

year  = {2022},

date = {2022-07-04},

urldate = {2021-01-01},

booktitle = {Advances in Neural Information Processing Systems},

journal = {arXiv preprint arXiv:2105.15013},

note = {Accepted at NeurIPS 2022 Conference},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}