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Offline Behavioral Data Selection
Shiye Lei, Zhihao Cheng, Dacheng Tao
Behavioral cloning is a widely adopted approach for offline policy learning from expert demonstrations. However, the large scale of offline behavioral datasets often results in com…
State Diversity Matters in Offline Behavior Distillation
Shiye Lei, Zhihao Cheng, Dacheng Tao
Offline Behavior Distillation (OBD), which condenses massive offline RL data into a compact synthetic behavioral dataset, offers a promising approach for efficient policy training…
Drawback of Enforcing Equivariance and its Compensation via the Lens of Expressive Power
Yuzhu Chen, Tian Qin, Xinmei Tian +2
Equivariant neural networks encode the intrinsic symmetry of data as an inductive bias, which has achieved impressive performance in wide domains. However, the understanding to the…
Offline Behavior Distillation
Shiye Lei, Sen Zhang, Dacheng Tao
Massive reinforcement learning (RL) data are typically collected to train policies offline without the need for interactions, but the large data volume can cause training inefficie…