Papers12:36Chinese contentHyperbolic IRL:用双曲距离参数化奖励函数的新方法把 IRL 的奖励函数改用双曲距离来算,数据少时效果更好,还能用博物馆导航数据解读人的行为,做模仿学习的朋友可以看看。#IRL#reinforcement learning#inverse reinforcement learningaarXiv cs.LG@Iulia Rusu et al. (4)SourceRead laterReadWorth followingUsefulFollow IRL
Papers15:34Chinese contentQVIRL:基于Q值的变分逆强化学习方法想学逆强化学习?QVIRL能边学边给不确定性,还能主动问你要数据,比老方法稳多了。#QVIRL#inverse reinforcement learning#Bayesian#active learningaarXiv cs.LG@Ondrej Bajgar et al. (5)SourceRead laterReadWorth followingUsefulFollow QVIRL
Papers18:12Chinese content高效超梯度下降法用于逆强化学习这篇论文给IRL的超梯度计算找了个巧妙的捷径,用Fisher矩阵和谱草图省下大量算力,做强化学习的朋友可以看看。#inverse reinforcement learning#Fisher information matrix#bilevel optimizationaarXiv cs.LG@Nikita Sevriukov et al. (7)SourceRead laterReadWorth followingUsefulFollow inverse reinforcement learning
Papers09:37Chinese content多模态多环境机器教学用于鲁棒奖励学习想解决机器人奖励函数跨环境失效的问题?这篇论文给出了数学分析和一种更聪明的教学策略,用更少反馈让奖励更鲁棒。#inverse reinforcement learning#IRL#reward learning#generalizationaarXiv cs.AI@Ali Larian et al. (4)SourceRead laterReadWorth followingUsefulFollow inverse reinforcement learning