1 code implementation • 21 Jan 2023 • Andrea Soltoggio, Eseoghene Ben-Iwhiwhu, Christos Peridis, Pawel Ladosz, Jeffery Dick, Praveen K. Pilly, Soheil Kolouri
This paper introduces a set of formally defined and transparent problems for reinforcement learning algorithms with the following characteristics: (1) variable degrees of observability (non-Markov observations), (2) distal and sparse rewards, (3) variable and hierarchical reward structure, (4) multiple-task generation, (5) variable problem complexity.
no code implementations • 2 May 2022 • Pawel Ladosz, Lilian Weng, Minwoo Kim, Hyondong Oh
This paper reviews exploration techniques in deep reinforcement learning.
1 code implementation • 27 Apr 2020 • Eseoghene Ben-Iwhiwhu, Pawel Ladosz, Jeffery Dick, Wen-Hua Chen, Praveen Pilly, Andrea Soltoggio
Rapid online adaptation to changing tasks is an important problem in machine learning and, recently, a focus of meta-reinforcement learning.
1 code implementation • 21 Sep 2019 • Pawel Ladosz, Eseoghene Ben-Iwhiwhu, Jeffery Dick, Yang Hu, Nicholas Ketz, Soheil Kolouri, Jeffrey L. Krichmar, Praveen Pilly, Andrea Soltoggio
This paper presents a new neural architecture that combines a modulated Hebbian network (MOHN) with DQN, which we call modulated Hebbian plus Q network architecture (MOHQA).