4 papers
Taming the Adversary: Stable Minimax Deep Deterministic Policy Gradient via Fractional Objectives
Taeho Lee, Donghwan Lee
Reinforcement learning (RL) has achieved remarkable success in a wide range of control and decision-making tasks. However, RL agents often exhibit unstable or degraded performance…
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…