no code implementations • 18 May 2022 • Jinwei Xing, Takashi Nagata, Xinyun Zou, Emre Neftci, Jeffrey L. Krichmar
Although deep Reinforcement Learning (RL) has proven successful in a wide range of tasks, one challenge it faces is interpretability when applied to real-world problems.
no code implementations • 25 Feb 2021 • Xinyun Zou, Eric O. Scott, Alexander B. Johnson, Kexin Chen, Douglas A. Nitz, Kenneth A. De Jong, Jeffrey L. Krichmar
Animals ranging from rats to humans can demonstrate cognitive map capabilities.
1 code implementation • 10 Feb 2021 • Jinwei Xing, Takashi Nagata, Kexin Chen, Xinyun Zou, Emre Neftci, Jeffrey L. Krichmar
To address this issue, we propose a two-stage RL agent that first learns a latent unified state representation (LUSR) which is consistent across multiple domains in the first stage, and then do RL training in one source domain based on LUSR in the second stage.
no code implementations • 14 Sep 2019 • Jinwei Xing, Xinyun Zou, Jeffrey L. Krichmar
In the present paper, we take inspiration from the serotonergic system and apply it to the task of robot navigation.
no code implementations • 2 Mar 2019 • Soheil Kolouri, Nicholas Ketz, Xinyun Zou, Jeffrey Krichmar, Praveen Pilly
Catastrophic forgetting/interference is a critical problem for lifelong learning machines, which impedes the agents from maintaining their previously learned knowledge while learning new tasks.
no code implementations • 16 Feb 2019 • Xinyun Zou, Soheil Kolouri, Praveen K. Pilly, Jeffrey L. Krichmar
In uncertain domains, the goals are often unknown and need to be predicted by the organism or system.