Goal-Space Planning with Subgoal Models

no code implementations • 6 Jun 2022 • Chunlok Lo, Kevin Roice, Parham Mohammad Panahi, Scott Jordan, Adam White, Gabor Mihucz, Farzane Aminmansour, Martha White

In this paper, we avoid this limitation by constraining background planning to a set of (abstract) subgoals and learning only local, subgoal-conditioned models.

Model-based Reinforcement Learning Reinforcement Learning (RL)

Soft Options Critic

no code implementations • 23 May 2019 • Elita Lobo, Scott Jordan

The option-critic architecture (Bacon, Harb, and Precup 2017) and several variants have successfully demonstrated the use of the options framework proposed by Sutton et al (Sutton, Precup, and Singh1999) to scale learning and planning in hierarchical tasks.

Learning Action Representations for Reinforcement Learning

no code implementations • 1 Feb 2019 • Yash Chandak, Georgios Theocharous, James Kostas, Scott Jordan, Philip S. Thomas

Most model-free reinforcement learning methods leverage state representations (embeddings) for generalization, but either ignore structure in the space of actions or assume the structure is provided a priori.

reinforcement-learning Reinforcement Learning (RL)