no code implementations • 11 Jun 2019 • René Traoré, Hugo Caselles-Dupré, Timothée Lesort, Te Sun, Natalia Díaz-Rodríguez, David Filliat
We focus on the problem of teaching a robot to solve tasks presented sequentially, i. e., in a continual learning scenario.
no code implementations • 11 Jul 2019 • René Traoré, Hugo Caselles-Dupré, Timothée Lesort, Te Sun, Guanghang Cai, Natalia Díaz-Rodríguez, David Filliat
In multi-task reinforcement learning there are two main challenges: at training time, the ability to learn different policies with a single model; at test time, inferring which of those policies applying without an external signal.
1 code implementation • 17 Sep 2021 • Zhaorun Chen, Siqi Fan, Yuan Tan, Liang Gong, Binhao Chen, Te Sun, David Filliat, Natalia Díaz-Rodríguez, Chengliang Liu
Firstly, We engage RL loss to assist in updating SRL model so that the states can evolve to meet the demand of RL and maintain a good physical interpretation.