5 citations · 13 across the 5 of their papers we have counts for
5 papers
Efficient Exploration through Intrinsic Motivation Learning for Unsupervised Subgoal Discovery in Model-Free Hierarchical Reinforcement Learning
Jacob Rafati, David C. Noelle
Efficient exploration for automatic subgoal discovery is a challenging problem in Hierarchical Reinforcement Learning (HRL). In this paper, we show that intrinsic motivation learni…
Quasi-Newton Optimization Methods For Deep Learning Applications
Jacob Rafati, Roummel F. Marcia
Deep learning algorithms often require solving a highly non-linear and nonconvex unconstrained optimization problem. Methods for solving optimization problems in large-scale machin…
Learning sparse representations in reinforcement learning
Jacob Rafati, David C. Noelle
Reinforcement learning (RL) algorithms allow artificial agents to improve their selection of actions to increase rewarding experiences in their environments. Temporal Difference (T…
Deep Reinforcement Learning via L-BFGS Optimization
Jacob Rafati, Roummel F. Marcia
Reinforcement Learning (RL) algorithms allow artificial agents to improve their action selections so as to increase rewarding experiences in their environments. Deep Reinforcement…
Learning Representations in Model-Free Hierarchical Reinforcement Learning
Jacob Rafati, David C. Noelle
Common approaches to Reinforcement Learning (RL) are seriously challenged by large-scale applications involving huge state spaces and sparse delayed reward feedback. Hierarchical R…