2 citations · 4 across the 7 of their papers we have counts for
7 papers
Adversarial Reinforcement Learning Framework for ESP Cheater Simulation
Inkyu Park, Jeong-Gwan Lee, Taehwan Kwon +4
Extra-Sensory Perception (ESP) cheats, which reveal hidden in-game information such as enemy locations, are difficult to detect because their effects are not directly observable in…
Hexa: Self-Improving for Knowledge-Grounded Dialogue System
Daejin Jo, Daniel Wontae Nam, Gunsoo Han +4
A common practice in knowledge-grounded dialogue generation is to explicitly utilize intermediate steps (e.g., web-search, memory retrieval) with modular approaches. However, data…
Effortless Integration of Memory Management into Open-Domain Conversation Systems
Eunbi Choi, Kyoung-Woon On, Gunsoo Han +6
Open-domain conversation systems integrate multiple conversation skills into a single system through a modular approach. One of the limitations of the system, however, is the absen…
LECO: Learnable Episodic Count for Task-Specific Intrinsic Reward
Daejin Jo, Sungwoong Kim, Daniel Wontae Nam +4
Episodic count has been widely used to design a simple yet effective intrinsic motivation for reinforcement learning with a sparse reward. However, the use of episodic count in a h…
Selective Token Generation for Few-shot Natural Language Generation
Daejin Jo, Taehwan Kwon, Eun-Sol Kim +1
Natural language modeling with limited training data is a challenging problem, and many algorithms make use of large-scale pretrained language models (PLMs) for this due to its gre…
Variational Intrinsic Control Revisited
Taehwan Kwon
In this paper, we revisit variational intrinsic control (VIC), an unsupervised reinforcement learning method for finding the largest set of intrinsic options available to an agent.…
Learning NP-Hard Multi-Agent Assignment Planning using GNN: Inference on a Random Graph and Provable Auction-Fitted Q-learning
Hyunwook Kang, Taehwan Kwon, Jinkyoo Park +1
This paper explores the possibility of near-optimally solving multi-agent, multi-task NP-hard planning problems with time-dependent rewards using a learning-based algorithm. In par…