15 citations · 32 across the 8 of their papers we have counts for
8 papers
Thespian: Multi-Character Text Role-Playing Game Agents
Christopher Cui, Xiangyu Peng, Mark Riedl
Text-adventure games and text role-playing games are grand challenges for reinforcement learning game playing agents. Text role-playing games are open-ended environments where an a…
Ambient Adventures: Teaching ChatGPT on Developing Complex Stories
Zexin Chen, Eric Zhou, Kenneth Eaton +2
Imaginative play is an area of creativity that could allow robots to engage with the world around them in a much more personified way. Imaginary play can be seen as taking real obj…
Dialogue Shaping: Empowering Agents through NPC Interaction
Wei Zhou, Xiangyu Peng, Mark Riedl
One major challenge in reinforcement learning (RL) is the large amount of steps for the RL agent needs to converge in the training process and learn the optimal policy, especially…
Story Shaping: Teaching Agents Human-like Behavior with Stories
Xiangyu Peng, Christopher Cui, Wei Zhou +2
Reward design for reinforcement learning agents can be difficult in situations where one not only wants the agent to achieve some effect in the world but where one also cares about…
Neuro-Symbolic World Models for Adapting to Open World Novelty
Jonathan Balloch, Zhiyu Lin, Robert Wright +5
Open-world novelty--a sudden change in the mechanics or properties of an environment--is a common occurrence in the real world. Novelty adaptation is an agent's ability to improve…
Divide to Adapt: Mitigating Confirmation Bias for Domain Adaptation of Black-Box Predictors
Jianfei Yang, Xiangyu Peng, Kai Wang +4
Domain Adaptation of Black-box Predictors (DABP) aims to learn a model on an unlabeled target domain supervised by a black-box predictor trained on a source domain. It does not req…