A Domain-Agnostic Approach for Characterization of Lifelong Learning Systems

no code implementations • 18 Jan 2023 • Megan M. Baker, Alexander New, Mario Aguilar-Simon, Ziad Al-Halah, Sébastien M. R. Arnold, Ese Ben-Iwhiwhu, Andrew P. Brna, Ethan Brooks, Ryan C. Brown, Zachary Daniels, Anurag Daram, Fabien Delattre, Ryan Dellana, Eric Eaton, Haotian Fu, Kristen Grauman, Jesse Hostetler, Shariq Iqbal, Cassandra Kent, Nicholas Ketz, Soheil Kolouri, George Konidaris, Dhireesha Kudithipudi, Erik Learned-Miller, Seungwon Lee, Michael L. Littman, Sandeep Madireddy, Jorge A. Mendez, Eric Q. Nguyen, Christine D. Piatko, Praveen K. Pilly, Aswin Raghavan, Abrar Rahman, Santhosh Kumar Ramakrishnan, Neale Ratzlaff, Andrea Soltoggio, Peter Stone, Indranil Sur, Zhipeng Tang, Saket Tiwari, Kyle Vedder, Felix Wang, Zifan Xu, Angel Yanguas-Gil, Harel Yedidsion, Shangqun Yu, Gautam K. Vallabha

Despite the advancement of machine learning techniques in recent years, state-of-the-art systems lack robustness to "real world" events, where the input distributions and tasks encountered by the deployed systems will not be limited to the original training context, and systems will instead need to adapt to novel distributions and tasks while deployed.

Model-based Lifelong Reinforcement Learning with Bayesian Exploration

2 code implementations • 20 Oct 2022 • Haotian Fu, Shangqun Yu, Michael Littman, George Konidaris

We propose a model-based lifelong reinforcement-learning approach that estimates a hierarchical Bayesian posterior distilling the common structure shared across different tasks.

reinforcement-learning Reinforcement Learning (RL)

Meta-Learning Parameterized Skills

1 code implementation • 7 Jun 2022 • Haotian Fu, Shangqun Yu, Saket Tiwari, Michael Littman, George Konidaris

We propose a novel parameterized skill-learning algorithm that aims to learn transferable parameterized skills and synthesize them into a new action space that supports efficient learning in long-horizon tasks.

Meta-Learning Robot Manipulation

Hierarchical Reinforcement Learning of Locomotion Policies in Response to Approaching Objects: A Preliminary Study

no code implementations • 20 Mar 2022 • Shangqun Yu, Sreehari Rammohan, Kaiyu Zheng, George Konidaris

Animals such as rabbits and birds can instantly generate locomotion behavior in reaction to a dynamic, approaching object, such as a person or a rock, despite having possibly never seen the object before and having limited perception of the object's properties.

Hierarchical Reinforcement Learning Object +2

Learning Generalizable Behavior via Visual Rewrite Rules

no code implementations • 9 Dec 2021 • Yiheng Xie, Mingxuan Li, Shangqun Yu, Michael Littman

Though deep reinforcement learning agents have achieved unprecedented success in recent years, their learned policies can be brittle, failing to generalize to even slight modifications of their environments or unfamiliar situations.

Descriptive

Bayesian Exploration for Lifelong Reinforcement Learning

no code implementations • 29 Sep 2021 • Haotian Fu, Shangqun Yu, Michael Littman, George Konidaris

A central question in reinforcement learning (RL) is how to leverage prior knowledge to accelerate learning in new tasks.

reinforcement-learning Reinforcement Learning (RL)

Value-Based Reinforcement Learning for Continuous Control Robotic Manipulation in Multi-Task Sparse Reward Settings

no code implementations • 28 Jul 2021 • Sreehari Rammohan, Shangqun Yu, Bowen He, Eric Hsiung, Eric Rosen, Stefanie Tellex, George Konidaris

Learning continuous control in high-dimensional sparse reward settings, such as robotic manipulation, is a challenging problem due to the number of samples often required to obtain accurate optimal value and policy estimates.

Continuous Control Data Augmentation +5