Tomoaki Oiki

TL;DR: A reinforcement learning-based algorithm for trajectory optimization for constrained dynamical systems, trained with a reference path and parameterize the policies with goal locations, so that the agent can be trained for multiple goals simultaneously.

TL;DR: In this paper, an online feature extractor network (OFENet) is proposed to produce good representations to be used as inputs to deep RL algorithms, and the authors show that the RL agents learn more efficiently with the high-dimensional representation than with the lower-dimensional state observations.

TL;DR: In this article, the authors presented a model that learns to move a marble in a complex environment within minutes of interacting with the real system, using a hybrid model consisting of a full physics engine along with a statistical function approximator.

TL;DR: In this paper, a reinforcement learning-based algorithm for trajectory optimization for constrained dynamical systems is proposed, which is motivated by the fact that for most robotic systems, the dynamics may not always be known.

TL;DR: This paper presents a model that learns to move a marble in the complex environment within minutes of interacting with the real system, and is the first time that a hybrid model consisting of a full physics engine along with a statistical function approximator has been used to control a complex physical system in real-time using nonlinear model-predictive control (NMPC).