Summary of Effective Exploration Based on the Structural Information Principles, by Xianghua Zeng et al.
Effective Exploration Based on the Structural Information Principlesby Xianghua Zeng, Hao Peng, Angsheng LiFirst submitted…
Summary of Q-wsl: Optimizing Goal-conditioned Rl with Weighted Supervised Learning Via Dynamic Programming, by Xing Lei et al.
Q-WSL: Optimizing Goal-Conditioned RL with Weighted Supervised Learning via Dynamic Programmingby Xing Lei, Xuetao Zhang,…
Summary of Flipping-based Policy For Chance-constrained Markov Decision Processes, by Xun Shen et al.
Flipping-based Policy for Chance-Constrained Markov Decision Processesby Xun Shen, Shuo Jiang, Akifumi Wachi, Kaumune Hashimoto,…
Summary of Honesty to Subterfuge: In-context Reinforcement Learning Can Make Honest Models Reward Hack, by Leo Mckee-reid et al.
Honesty to Subterfuge: In-Context Reinforcement Learning Can Make Honest Models Reward Hackby Leo McKee-Reid, Christoph…
Summary of Quality Diversity Imitation Learning, by Zhenglin Wan et al.
Quality Diversity Imitation Learningby Zhenglin Wan, Xingrui Yu, David Mark Bossens, Yueming Lyu, Qing Guo,…
Summary of Solving Robust Mdps As a Sequence Of Static Rl Problems, by Adil Zouitine et al.
Solving robust MDPs as a sequence of static RL problemsby Adil Zouitine, Matthieu Geist, Emmanuel…
Summary of Rl, but Don’t Do Anything I Wouldn’t Do, by Michael K. Cohen et al.
RL, but don’t do anything I wouldn’t doby Michael K. Cohen, Marcus Hutter, Yoshua Bengio,…
Summary of Efficient Policy Evaluation with Safety Constraint For Reinforcement Learning, by Claire Chen et al.
Efficient Policy Evaluation with Safety Constraint for Reinforcement Learningby Claire Chen, Shuze Liu, Shangtong ZhangFirst…
Summary of Reinforcement Learning From Imperfect Corrective Actions and Proxy Rewards, by Zhaohui Jiang et al.
Reinforcement Learning From Imperfect Corrective Actions And Proxy Rewardsby Zhaohui Jiang, Xuening Feng, Paul Weng,…
Summary of Llms Are In-context Bandit Reinforcement Learners, by Giovanni Monea et al.
LLMs Are In-Context Bandit Reinforcement Learnersby Giovanni Monea, Antoine Bosselut, Kianté Brantley, Yoav ArtziFirst submitted…