2 citations · 4 across the 4 of their papers we have counts for
4 papers
Next-Depth Lookahead Tree
Jaeho Lee, Kangjin Kim, Gyeong Taek Lee
This paper proposes the Next-Depth Lookahead Tree (NDLT), a single-tree model designed to improve performance by evaluating node splits not only at the node being optimized but als…
A Fully Controllable Agent in the Path Planning using Goal-Conditioned Reinforcement Learning
GyeongTaek Lee
The aim of path planning is to reach the goal from starting point by searching for the route of an agent. In the path planning, the routes may vary depending on the number of varia…
Learning user-defined sub-goals using memory editing in reinforcement learning
GyeongTaek Lee
The aim of reinforcement learning (RL) is to allow the agent to achieve the final goal. Most RL studies have focused on improving the efficiency of learning to achieve the final go…
Amplifying the Imitation Effect for Reinforcement Learning of UCAV's Mission Execution
Gyeong Taek Lee, Chang Ouk Kim
This paper proposes a new reinforcement learning (RL) algorithm that enhances exploration by amplifying the imitation effect (AIE). This algorithm consists of self-imitation learni…