Efficient Exploration through Intrinsic Motivation Learning for Unsupervised Subgoal Discovery in Model-Free Hierarchical Reinforcement Learning

no code implementations • 18 Nov 2019 • Jacob Rafati, David C. Noelle

Efficient exploration for automatic subgoal discovery is a challenging problem in Hierarchical Reinforcement Learning (HRL).

Efficient Exploration Hierarchical Reinforcement Learning +2

Learning sparse representations in reinforcement learning

no code implementations • 4 Sep 2019 • Jacob Rafati, David C. Noelle

This has motivated methods that learn internal representations of the agent's state, effectively reducing the size of the state space and restructuring state representations in order to support generalization.

Acrobot reinforcement-learning +1

Quasi-Newton Optimization Methods For Deep Learning Applications

no code implementations • 4 Sep 2019 • Jacob Rafati, Roummel F. Marcia

Quasi-Newton methods, like SGD, require only first-order gradient information, but they can result in superlinear convergence, which makes them attractive alternatives to SGD.

Image Classification reinforcement-learning +2

Deep Reinforcement Learning via L-BFGS Optimization

no code implementations • 6 Nov 2018 • Jacob Rafati, Roummel F. Marcia

Deep Reinforcement Learning algorithms require solving a nonconvex and nonlinear unconstrained optimization problem.

Atari Games Q-Learning +3

Learning Representations in Model-Free Hierarchical Reinforcement Learning

no code implementations • 23 Oct 2018 • Jacob Rafati, David C. Noelle

When combined with an intrinsic motivation learning mechanism, this method learns both subgoals and skills, based on experiences in the environment.

Hierarchical Reinforcement Learning Montezuma's Revenge +2