Explicit Planning for Efficient Exploration in Reinforcement Learning

no code implementations • NeurIPS 2019 • Liangpeng Zhang, Ke Tang, Xin Yao

We argue that explicit planning for exploration can help alleviate such a problem, and propose a Value Iteration for Exploration Cost (VIEC) algorithm which computes the optimal exploration scheme by solving an augmented MDP.

Efficient Exploration reinforcement-learning +1

Log-normality and Skewness of Estimated State/Action Values in Reinforcement Learning

no code implementations • NeurIPS 2017 • Liangpeng Zhang, Ke Tang, Xin Yao

Under/overestimation of state/action values are harmful for reinforcement learning agents.

reinforcement-learning Reinforcement Learning (RL)

Success Probability of Exploration: a Concrete Analysis of Learning Efficiency

no code implementations • 2 Dec 2016 • Liangpeng Zhang, Ke Tang, Xin Yao

We then provide empirical results to verify our approach, and demonstrate how the success probability of exploration can be used to analyse and predict the behaviours and possible outcomes of exploration, which are the keys to the answer of the important questions of exploration.

Relief R-CNN : Utilizing Convolutional Features for Fast Object Detection

1 code implementation • 25 Jan 2016 • Guiying Li, Junlong Liu, Chunhui Jiang, Liangpeng Zhang, Minlong Lin, Ke Tang

R-CNN style methods are sorts of the state-of-the-art object detection methods, which consist of region proposal generation and deep CNN classification.

object-detection Real-Time Object Detection +1