2 citations · 2 across the 3 of their papers we have counts for
4 papers
Logical Specifications-guided Dynamic Task Sampling for Reinforcement Learning Agents
Yash Shukla, Tanushree Burman, Abhishek Kulkarni +3
Reinforcement Learning (RL) has made significant strides in enabling artificial agents to learn diverse behaviors. However, learning an effective policy often requires a large numb…
LgTS: Dynamic Task Sampling using LLM-generated sub-goals for Reinforcement Learning Agents
Yash Shukla, Wenchang Gao, Vasanth Sarathy +3
Recent advancements in reasoning abilities of Large Language Models (LLM) has promoted their usage in problems that require high-level planning for robots and artificial agents. Ho…
A Framework for Few-Shot Policy Transfer through Observation Mapping and Behavior Cloning
Yash Shukla, Bharat Kesari, Shivam Goel +2
Despite recent progress in Reinforcement Learning for robotics applications, many tasks remain prohibitively difficult to solve because of the expensive interaction cost. Transfer…
Novelty Detection in Reinforcement Learning with World Models
Geigh Zollicoffer, Kenneth Eaton, Jonathan Balloch +4
Reinforcement learning (RL) using world models has found significant recent successes. However, when a sudden change to world mechanics or properties occurs then agent performance…