4 papers
Taming the Adversary: A Cost-to-Disturbance Ratio Approach to Adversarial Reinforcement Learning
Taeho Lee, Donghwan Lee
Reinforcement learning (RL) policies trained in simulation often degrade once deployed on real systems, where the controller must reject external disturbances that were never encou…
TruncQuant: Truncation-Ready Quantization for DNNs with Flexible Weight Bit Precision
Jinhee Kim, Seoyeon Yoon, Taeho Lee +3
The deployment of deep neural networks on edge devices is a challenging task due to the increasing complexity of state-of-the-art models, requiring efforts to reduce model size and…
Deep Q-Learning with Gradient Target Tracking
Bum Geun Park, Taeho Lee, Donghwan Lee
This paper introduces Q-learning with gradient target tracking, a novel reinforcement learning framework that provides a learned continuous target update mechanism as an alternativ…
Robust Deterministic Policy Gradient for Disturbance Attenuation and Its Application to Quadrotor Control
Taeho Lee, Donghwan Lee
This paper presents a robust reinforcement learning algorithm called robust deterministic policy gradient (RDPG), which reformulates the H-infinity control problem as a two-player…