34 citations · 89 across the 18 of their papers we have counts for
3 papers · 1 filter
Relative Behavioral Attributes: Filling the Gap between Symbolic Goal Specification and Reward Learning from Human Preferences
Lin Guan, Karthik Valmeekam, Subbarao Kambhampati
Generating complex behaviors that satisfy the preferences of non-expert users is a crucial requirement for AI agents. Interactive reward learning from trajectory comparisons (a.k.a…
Towards customizable reinforcement learning agents: Enabling preference specification through online vocabulary expansion
Utkarsh Soni, Nupur Thakur, Sarath Sreedharan +4
There is a growing interest in developing automated agents that can work alongside humans. In addition to completing the assigned task, such an agent will undoubtedly be expected t…
Leveraging Approximate Symbolic Models for Reinforcement Learning via Skill Diversity
Lin Guan, Sarath Sreedharan, Subbarao Kambhampati
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 dev…