4 papers
MAPL: Multi-Objective Preference Learning for Robot Locomotion
Xiyue Chen, Muhan Lin, Shuyang Shi +1
Reward design remains a major bottleneck in reinforcement learning for robot locomotion, where successful policies often depend on carefully tuned, task-specific reward functions.…
HyperAdapt: Simple High-Rank Adaptation
Abel Gurung, Joseph Campbell
Foundation models excel across diverse tasks, but adapting them to specialized applications often requires fine-tuning, an approach that is memory and compute-intensive. Parameter-…
Bayesian Social Deduction with Graph-Informed Language Models
Shahab Rahimirad, Guven Gergerli, Lucia Romero +4
Social reasoning - inferring unobservable beliefs and intentions from partial observations of other agents - remains a challenging task for large language models (LLMs). We evaluat…
CDE: Concept-Driven Exploration for Reinforcement Learning
Le Mao, Andrew H. Liu, Renos Zabounidis +3
Intelligent exploration remains a critical challenge in reinforcement learning (RL), especially in visual control tasks. Unlike low-dimensional state-based RL, visual RL must extra…