36 citations · 83 across the 13 of their papers we have counts for
6 papers · 1 filter
Generative Adversarial Self-Imitation Learning
Yijie Guo, Junhyuk Oh, Satinder Singh +1
This paper explores a simple regularizer for reinforcement learning by proposing Generative Adversarial Self-Imitation Learning (GASIL), which encourages the agent to imitate past…
Learning End-to-End Goal-Oriented Dialog with Multiple Answers
Janarthanan Rajendran, Jatin Ganhotra, Satinder Singh +1
In a dialog, there can be multiple valid next utterances at any point. The present end-to-end neural methods for dialog do not take this into account. They learn with the assumptio…
Many-Goals Reinforcement Learning
Vivek Veeriah, Junhyuk Oh, Satinder Singh
All-goals updating exploits the off-policy nature of Q-learning to update all possible goals an agent could have from each transition in the world, and was introduced into Reinforc…
Self-Imitation Learning
Junhyuk Oh, Yijie Guo, Satinder Singh +1
This paper proposes Self-Imitation Learning (SIL), a simple off-policy actor-critic algorithm that learns to reproduce the agent's past good decisions. This algorithm is designed t…
NE-Table: A Neural key-value table for Named Entities
Janarthanan Rajendran, Jatin Ganhotra, Xiaoxiao Guo +3
Many Natural Language Processing (NLP) tasks depend on using Named Entities (NEs) that are contained in texts and in external knowledge sources. While this is easy for humans, the…
The Advantage of Doubling: A Deep Reinforcement Learning Approach to Studying the Double Team in the NBA
Jiaxuan Wang, Ian Fox, Jonathan Skaza +3
During the 2017 NBA playoffs, Celtics coach Brad Stevens was faced with a difficult decision when defending against the Cavaliers: "Do you double and risk giving up easy shots, or…