Search Results for author: Abhishek Kulkarni

Found 3 papers, 1 papers with code

Automaton-Guided Curriculum Generation for Reinforcement Learning Agents

1 code implementation11 Apr 2023 Yash Shukla, Abhishek Kulkarni, Robert Wright, Alvaro Velasquez, Jivko Sinapov

Experiments in gridworld and physics-based simulated robotics domains show that the curricula produced by AGCL achieve improved time-to-threshold performance on a complex sequential decision-making problem relative to state-of-the-art curriculum learning (e. g, teacher-student, self-play) and automaton-guided reinforcement learning baselines (e. g, Q-Learning for Reward Machines).

Decision Making Q-Learning +2

Logical Specifications-guided Dynamic Task Sampling for Reinforcement Learning Agents

no code implementations6 Feb 2024 Yash Shukla, Tanushree Burman, Abhishek Kulkarni, Robert Wright, Alvaro Velasquez, Jivko Sinapov

In this work, we propose a novel approach, called Logical Specifications-guided Dynamic Task Sampling (LSTS), that learns a set of RL policies to guide an agent from an initial state to a goal state based on a high-level task specification, while minimizing the number of environmental interactions.

Continuous Control Decision Making +3

Cannot find the paper you are looking for? You can Submit a new open access paper.