Creating reinforcement learning (RL) agents that are capable of accepting and leveraging task-specific knowledge from humans has been long identified as a possible strategy for developing scalable approaches for solving long-horizon problems. While previous works have looked at the possibility of using symbolic models along with RL approaches, they tend to assume that the high-level action models are executable at low level and the fluents can exclusively characterize all desirable MDP states. Symbolic models of real world tasks are however often incomplete. To this end, we introduce Approximate Symbolic-Model Guided Reinforcement Learning, wherein we will formalize the relationship between the symbolic model and the underlying MDP that will allow us to characterize the incompleteness of the symbolic model. We will use these models to extract high-level landmarks that will be used to decompose the task. At the low level, we learn a set of diverse policies for each possible task subgoal identified by the landmark, which are then stitched together. We evaluate our system by testing on three different benchmark domains and show how even with incomplete symbolic model information, our approach is able to discover the task structure and efficiently guide the RL agent towards the goal.
ComSD: Balancing Behavioral Quality and Diversity in Unsupervised Skill Discovery
Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）；University of Chinese Academy of Sciences（中国科学院大学）；Nanjing Institute of Software Technology（南京软件技术研究院）
Department of Computer Science（计算机科学系）；Vrije Universiteit Amsterdam（阿姆斯特丹自由大学）；Leiden University（莱顿大学）；The Leiden Institute of Advanced Computer Science（莱顿先进计算机科学研究所）；University of Pennsylvania（宾夕法尼亚大学）；GRASP Lab（GRASP实验室）
Skill Learning via Policy Diversity Yields Identifiable Representations for Reinforcement Learning
Max Planck Institute for Intelligent Systems（马克斯·普朗克智能系统研究所）；ELLIS Institute Tübingen（图宾根ELLIS研究所）；University of Tübingen（图宾根大学）；Tübingen AI Center（图宾根人工智能中心）；University of Cambridge（剑桥大学）；Princeton University（普林斯顿大学）
本文从可辨识表征学习角度研究互信息技能学习，证明对比后继特征方法可在线性变换意义下可证明地恢复环境真实特征，并在 MuJoCo 与 DeepMind Control 上验证了该结论。
Beyond Top-$k$ Skill Retrieval: Diversity-Aware Skill Routing for LLM Agents
Virginia Tech（弗吉尼亚理工大学）；University of Southern California（南加州大学）；Adobe Research（Adobe研究院）；Dolby Labs（杜比实验室）；Texas A&M University（德克萨斯A&M大学）；Arizona State University（亚利桑那州立大学）
Lifting Traces to Logic: Programmatic Skill Induction with Neuro-Symbolic Learning for Long-Horizon Agentic Tasks
State Key Laboratory of Novel Software Technology, Nanjing University, China（新型软件技术国家重点实验室，南京大学）；School of Artificial Intelligence, Nanjing University, China（人工智能学院，南京大学）；School of Intelligence Science and Technology, Nanjing University, China（智能科学与技术学院，南京大学）；Centre for Frontier AI Research（前沿人工智能研究中心）；Institute of High Performance Computing, Agency for Science, Technology and Research, Singapore（高性能计算研究所，新加坡科技研究局）；Department of Computer Science and Engineering, Hong Kong University of Science and Technology, China（香港科学与技术大学计算机科学与工程系）
Neuro-Symbolic Imitation Learning: Discovering Symbolic Abstractions for Skill Learning
Intelligent Autonomous Systems, TU Darmstadt, Germany（图腾达姆施塔特大学智能自主系统研究所）；German Research Center for AI, Germany（德国人工智能研究中心）；Hessian Centre for Artificial Intelligence, Germany（黑森州人工智能中心）；Honda Research Institute EU, Germany（本田欧洲研究院）
Automated discovery of symbolic laws governing skill acquisition from naturally occurring data
National Engineering Research Center of Educational Big Data, Central China Normal University（华中师范大学教育大数据国家工程研究中心）；Faculty of Artificial Intelligence in Education, Central China Normal University（华中师范大学人工智能教育学部）；National Engineering Research Center for E-learning, Central China Normal University（华中师范大学国家数字化学习工程技术研究中心）
Neuro-Symbolic Skill Discovery for Conditional Multi-Level Planning
Department of Computer Science and Technology The University of Cambridge（计算机科学与技术系 剑桥大学）；Department of Computer Engineering Bogazici University（计算机工程系 boazici大学）；SISREC Osaka University（Osaka大学SISREC）
An efficient machine learning approach for extracting eSports players distinguishing features and classifying their skill levels using symbolic transfer entropy and consensus nested cross validation
Staffordshire University（斯塔福德郡大学）；New York Institute of Technology（纽约理工学院）；Azad University（阿扎德大学）
AI4S-SDS: A Neuro-Symbolic Solvent Design System via Sparse MCTS and Differentiable Physics Alignment
State Key Laboratory for Novel Software Technology at Nanjing University（南京大学新型软件技术国家重点实验室）；School of Computer Science, Nanjing University（南京大学计算机科学学院）；Suzhou Laboratory（苏州实验室）
Efficient Skill Discovery via Regret-Aware Optimization
Shanghai AI Lab（上海人工智能实验室）；Thrust of Artificial Intelligence, The Hong Kong University of Science（人工智能研究方向，香港科学与技术大学）；Department of Computer Science（计算机科学系）；Engineering, The Hong Kong University of Science（工程，香港科学与技术大学）
