6 citations · 8 across the 3 of their papers we have counts for
5 papers
GAN-Based Interactive Reinforcement Learning from Demonstration and Human Evaluative Feedback
Jie Huang, Rongshun Juan, Randy Gomez +4
Deep reinforcement learning (DRL) has achieved great successes in many simulated tasks. The sample inefficiency problem makes applying traditional DRL methods to real-world robots…
Facial Feedback for Reinforcement Learning: A Case Study and Offline Analysis Using the TAMER Framework
Guangliang Li, Hamdi Dibeklioğlu, Shimon Whiteson +1
Interactive reinforcement learning provides a way for agents to learn to solve tasks from evaluative feedback provided by a human user. Previous research showed that humans give co…
Deep Interactive Reinforcement Learning for Path Following of Autonomous Underwater Vehicle
Qilei Zhang, Jinying Lin, Qixin Sha +2
Autonomous underwater vehicle (AUV) plays an increasingly important role in ocean exploration. Existing AUVs are usually not fully autonomous and generally limited to pre-planning…
Improving Interactive Reinforcement Agent Planning with Human Demonstration
Guangliang Li, Randy Gomez, Keisuke Nakamura +3
TAMER has proven to be a powerful interactive reinforcement learning method for allowing ordinary people to teach and personalize autonomous agents' behavior by providing evaluativ…
Learning Shaping Strategies in Human-in-the-loop Interactive Reinforcement Learning
Chao Yu, Tianpei Yang, Wenxuan Zhu +2
Providing reinforcement learning agents with informationally rich human knowledge can dramatically improve various aspects of learning. Prior work has developed different kinds of…