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.

System Design for an Integrated Lifelong Reinforcement Learning Agent for Real-Time Strategy Games

no code implementations • 8 Dec 2022 • Indranil Sur, Zachary Daniels, Abrar Rahman, Kamil Faber, Gianmarco J. Gallardo, Tyler L. Hayes, Cameron E. Taylor, Mustafa Burak Gurbuz, James Smith, Sahana Joshi, Nathalie Japkowicz, Michael Baron, Zsolt Kira, Christopher Kanan, Roberto Corizzo, Ajay Divakaran, Michael Piacentino, Jesse Hostetler, Aswin Raghavan

In this paper, we introduce the Lifelong Reinforcement Learning Components Framework (L2RLCF), which standardizes L2RL systems and assimilates different continual learning components (each addressing different aspects of the lifelong learning problem) into a unified system.

Continual Learning reinforcement-learning +2

Systematization of Knowledge: Synthetic Assets, Derivatives, and On-Chain Portfolio Management

no code implementations • 20 Sep 2022 • Abrar Rahman, Victor Shi, Matthew Ding, Elliot Choi

Synthetic assets are decentralized finance (DeFi) analogues of derivatives in the traditional finance (TradFi) world - financial arrangements which derive value from and are directly pegged to fluctuations in the value of an underlying asset (ex: futures and options).

Management

Model-Free Generative Replay for Lifelong Reinforcement Learning: Application to Starcraft-2

no code implementations • 9 Aug 2022 • Zachary Daniels, Aswin Raghavan, Jesse Hostetler, Abrar Rahman, Indranil Sur, Michael Piacentino, Ajay Divakaran

We present a version of GR for LRL that satisfies two desiderata: (a) Introspective density modelling of the latent representations of policies learned using deep RL, and (b) Model-free end-to-end learning.

Management reinforcement-learning +3

Lifelong Learning using Eigentasks: Task Separation, Skill Acquisition, and Selective Transfer

no code implementations • 14 Jul 2020 • Aswin Raghavan, Jesse Hostetler, Indranil Sur, Abrar Rahman, Ajay Divakaran

We propose a wake-sleep cycle of alternating task learning and knowledge consolidation for learning in our framework, and instantiate it for lifelong supervised learning and lifelong RL.

Continual Learning Transfer Learning

Lifelong Learning using Eigentasks: Task Separation, Skill Acquisition and Selective Transfer

no code implementations • ICML Workshop LifelongML 2020 • Aswin Raghavan, Jesse Hostetler, Indranil Sur, Abrar Rahman, Ajay Divakaran

We propose a wake-sleep cycle of alternating task learning and knowledge consolidation for learning in our framework, and instantiate it for lifelong supervised learning and lifelong RL.

Continual Learning Starcraft +1