1 citations · 2 across the 9 of their papers we have counts for
9 papers
Virtual Action Actor-Critic Framework for Exploration (Student Abstract)
Bumgeun Park, Taeyoung Kim, Quoc-Vinh Lai-Dang +1
Efficient exploration for an agent is challenging in reinforcement learning (RL). In this paper, a novel actor-critic framework namely virtual action actor-critic (VAAC), is propos…
Enhanced Transformer Architecture for Natural Language Processing
Woohyeon Moon, Taeyoung Kim, Bumgeun Park +1
Transformer is a state-of-the-art model in the field of natural language processing (NLP). Current NLP models primarily increase the number of transformers to improve processing pe…
Road Redesign Technique Achieving Enhanced Road Safety by Inpainting with a Diffusion Model
Sumit Mishra, Medhavi Mishra, Taeyoung Kim +1
Road infrastructure can affect the occurrence of road accidents. Therefore, identifying roadway features with high accident probability is crucial. Here, we introduce image inpaint…
Off-Policy Reinforcement Learning with Loss Function Weighted by Temporal Difference Error
Bumgeun Park, Taeyoung Kim, Woohyeon Moon +2
Training agents via off-policy deep reinforcement learning (RL) requires a large memory, named replay memory, that stores past experiences used for learning. These experiences are…
Reinforcement Learning for Predicting Traffic Accidents
Injoon Cho, Praveen Kumar Rajendran, Taeyoung Kim +1
As the demand for autonomous driving increases, it is paramount to ensure safety. Early accident prediction using deep learning methods for driving safety has recently gained much…
Kick-motion Training with DQN in AI Soccer Environment
Bumgeun Park, Jihui Lee, Taeyoung Kim +1
This paper presents a technique to train a robot to perform kick-motion in AI soccer by using reinforcement learning (RL). In RL, an agent interacts with an environment and learns…
Cluster-based Sampling in Hindsight Experience Replay for Robotic Tasks (Student Abstract)
Taeyoung Kim, Dongsoo Har
In multi-goal reinforcement learning with a sparse binary reward, training agents is particularly challenging, due to a lack of successful experiences. To solve this problem, hinds…
Path Planning of Cleaning Robot with Reinforcement Learning
Woohyeon Moon, Bumgeun Park, Sarvar Hussain Nengroo +2
Recently, as the demand for cleaning robots has steadily increased, therefore household electricity consumption is also increasing. To solve this electricity consumption issue, the…
Two-stage training algorithm for AI robot soccer
Taeyoung Kim, Luiz Felipe Vecchietti, Kyujin Choi +2
In multi-agent reinforcement learning, the cooperative learning behavior of agents is very important. In the field of heterogeneous multi-agent reinforcement learning, cooperative…