Presentation information

General Session

General Session » [GS] J-2 Machine learning

[1Q2-J-2] Machine learning: reinforcement learning and its advances

Tue. Jun 4, 2019 1:20 PM - 3:00 PM Room Q (6F Meeting room, Bandaijima bldg.)

Chair:Koichiro Yoshino Reviewer:Kohei Miyaguchi

1:20 PM - 1:40 PM

[1Q2-J-2-01] Building a Human-Like Agent Based on a Hybrid of Reinforcement and Imitation Learning

〇Rousslan Fernand Julien Dossa1, Xinyu Lian1, Hirokazu Nomoto2, Takashi Matsubara1, Kuniaki Uehara1 (1. Kobe University, 2. EQUOS RESEARCH Co., Ltd.)

Keywords:Reinforcement Learning, Imitation Learning, Human Likeliness

Reinforcement learning (RL) builds an effective agent that handles tasks in complex and uncertain environments by maximizing future reward.
However,the efficiency is insufficient for practical use such as game AI and autonomous driving.
An effective but selfish agent conflicts with other humans,and hence the demand of a human-like behavior arises.
Imitation learning (IL) has been employed to trains an agent to mimic the actions of expert behaviors provided as training data.
However,IL tends to build an agent limited in performance by the expert skill,and even worse,the agent exhibits an inconsistent behavior since IL is not goal-oriented.
In this paper,we propose a training scheme by mixing RL and IL for both discrete and continuous action space problems.
The proposed scheme builds an agent that achieves a performance higher than an agent trained by only IL and exhibits a more human-like behavior than agents trained by RL or IL,validated by human sensitivity.