Search Results for author: Takayuki Osa

Found 9 papers, 3 papers with code

Motion Planning by Learning the Solution Manifold in Trajectory Optimization

no code implementations13 Jul 2021 Takayuki Osa

The experimental results indicate that the trained model represents an infinite set of homotopic solutions for motion planning problems.

Motion Planning

Discovering Diverse Solutions in Deep Reinforcement Learning by Maximizing State-Action-Based Mutual Information

1 code implementation12 Mar 2021 Takayuki Osa, Voot Tangkaratt, Masashi Sugiyama

In our method, a policy conditioned on a continuous or discrete latent variable is trained by directly maximizing the variational lower bound of the mutual information, instead of using the mutual information as unsupervised rewards as in previous studies.

Continuous Control

Learning the Solution Manifold in Optimization and Its Application in Motion Planning

no code implementations24 Jul 2020 Takayuki Osa

The experimental results indicate that the solution manifold can be learned with the proposed algorithm, and the trained model represents an infinite set of homotopic solutions for motion-planning problems.

Density Estimation Motion Planning

Meta-Model-Based Meta-Policy Optimization

no code implementations4 Jun 2020 Takuya Hiraoka, Takahisa Imagawa, Voot Tangkaratt, Takayuki Osa, Takashi Onishi, Yoshimasa Tsuruoka

Model-based meta-reinforcement learning (RL) methods have recently been shown to be a promising approach to improving the sample efficiency of RL in multi-task settings.

Continuous Control Meta-Learning +3

Goal-Conditioned Variational Autoencoder Trajectory Primitives with Continuous and Discrete Latent Codes

no code implementations9 Dec 2019 Takayuki Osa, Shuhei Ikemoto

The learned decoder can be used as a motion planner in which the user can specify the goal position and the trajectory types by setting the latent variables.

Data Augmentation Imitation Learning +1

Reducing Overestimation Bias in Multi-Agent Domains Using Double Centralized Critics

2 code implementations3 Oct 2019 Johannes Ackermann, Volker Gabler, Takayuki Osa, Masashi Sugiyama

Finally, we investigate the application of multi-agent methods to high-dimensional robotic tasks and show that our approach can be used to learn decentralized policies in this domain.

Multi-agent Reinforcement Learning Reinforcement Learning (RL)

An Algorithmic Perspective on Imitation Learning

no code implementations16 Nov 2018 Takayuki Osa, Joni Pajarinen, Gerhard Neumann, J. Andrew Bagnell, Pieter Abbeel, Jan Peters

This process of learning from demonstrations, and the study of algorithms to do so, is called imitation learning.

Imitation Learning Learning Theory

Hierarchical Policy Search via Return-Weighted Density Estimation

no code implementations28 Nov 2017 Takayuki Osa, Masashi Sugiyama

Learning an optimal policy from a multi-modal reward function is a challenging problem in reinforcement learning (RL).

Density Estimation Motion Planning +1

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