Search Results for author: Te Sun

Found 3 papers, 1 papers with code

POAR: Efficient Policy Optimization via Online Abstract State Representation Learning

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.

reinforcement-learning Reinforcement Learning (RL) +1

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Continual Reinforcement Learning deployed in Real-life using Policy Distillation and Sim2Real Transfer

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.

Continual Learning reinforcement-learning +2

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DisCoRL: Continual Reinforcement Learning via Policy Distillation

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.

reinforcement-learning Reinforcement Learning (RL) +1

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