Summary of Learning Diverse Policies with Soft Self-generated Guidance, by Guojian Wang et al.
Learning Diverse Policies with Soft Self-Generated Guidanceby Guojian Wang, Faguo Wu, Xiao Zhang, Jianxiang LiuFirst…
Summary of Oil-ad: An Anomaly Detection Framework For Sequential Decision Sequences, by Chen Wang et al.
OIL-AD: An Anomaly Detection Framework for Sequential Decision Sequencesby Chen Wang, Sarah Erfani, Tansu Alpcan,…
Summary of A Primal-dual Algorithm For Offline Constrained Reinforcement Learning with Linear Mdps, by Kihyuk Hong et al.
A Primal-Dual Algorithm for Offline Constrained Reinforcement Learning with Linear MDPsby Kihyuk Hong, Ambuj TewariFirst…
Summary of Read to Play (r2-play): Decision Transformer with Multimodal Game Instruction, by Yonggang Jin et al.
Read to Play (R2-Play): Decision Transformer with Multimodal Game Instructionby Yonggang Jin, Ge Zhang, Hao…
Summary of Informed Reinforcement Learning For Situation-aware Traffic Rule Exceptions, by Daniel Bogdoll et al.
Informed Reinforcement Learning for Situation-Aware Traffic Rule Exceptionsby Daniel Bogdoll, Jing Qin, Moritz Nekolla, Ahmed…
Summary of Reinforcement Learning with Ensemble Model Predictive Safety Certification, by Sven Gronauer et al.
Reinforcement Learning with Ensemble Model Predictive Safety Certificationby Sven Gronauer, Tom Haider, Felippe Schmoeller da…
Summary of Musicrl: Aligning Music Generation to Human Preferences, by Geoffrey Cideron et al.
MusicRL: Aligning Music Generation to Human Preferencesby Geoffrey Cideron, Sertan Girgin, Mauro Verzetti, Damien Vincent,…
Summary of Entropy-regularized Diffusion Policy with Q-ensembles For Offline Reinforcement Learning, by Ruoqi Zhang et al.
Entropy-regularized Diffusion Policy with Q-Ensembles for Offline Reinforcement Learningby Ruoqi Zhang, Ziwei Luo, Jens Sjölund,…
Summary of Return-aligned Decision Transformer, by Tsunehiko Tanaka et al.
Return-Aligned Decision Transformerby Tsunehiko Tanaka, Kenshi Abe, Kaito Ariu, Tetsuro Morimura, Edgar Simo-SerraFirst submitted to…
Summary of In-context Learning Agents Are Asymmetric Belief Updaters, by Johannes A. Schubert et al.
In-context learning agents are asymmetric belief updatersby Johannes A. Schubert, Akshay K. Jagadish, Marcel Binz,…