Search Results for author: Jianyu Chen

Found 23 papers, 9 papers with code

Reachability Constrained Reinforcement Learning

no code implementations16 May 2022 Dongjie Yu, Haitong Ma, Shengbo Eben Li, Jianyu Chen

We characterize the feasible set by the established self-consistency condition, then a safety value function can be learned and used as constraints in CRL.

reinforcement-learning

Scale-Equivalent Distillation for Semi-Supervised Object Detection

no code implementations23 Mar 2022 Qiushan Guo, Yao Mu, Jianyu Chen, Tianqi Wang, Yizhou Yu, Ping Luo

Further, we overcome these challenges by introducing a novel approach, Scale-Equivalent Distillation (SED), which is a simple yet effective end-to-end knowledge distillation framework robust to large object size variance and class imbalance.

Knowledge Distillation Object Detection +1

Zeroth-Order Actor-Critic

no code implementations29 Jan 2022 YuHeng Lei, Jianyu Chen, Shengbo Eben Li, Sifa Zheng

Zeroth-order optimization methods and policy gradient based first-order methods are two promising alternatives to solve reinforcement learning (RL) problems with complementary advantages.

Continuous Control

Learn Zero-Constraint-Violation Policy in Model-Free Constrained Reinforcement Learning

no code implementations25 Nov 2021 Haitong Ma, Changliu Liu, Shengbo Eben Li, Sifa Zheng, Wenchao Sun, Jianyu Chen

Existing methods mostly use the posterior penalty for dangerous actions, which means that the agent is not penalized until experiencing danger.

reinforcement-learning

Joint Synthesis of Safety Certificate and Safe Control Policy using Constrained Reinforcement Learning

no code implementations15 Nov 2021 Haitong Ma, Changliu Liu, Shengbo Eben Li, Sifa Zheng, Jianyu Chen

This paper proposes a novel approach that simultaneously synthesizes the energy-function-based safety certificate and learns the safe control policy with CRL.

reinforcement-learning

Flow-based Recurrent Belief State Learning for POMDPs

no code implementations29 Sep 2021 Xiaoyu Chen, Yao Mu, Ping Luo, Shengbo Eben Li, Jianyu Chen

Partially Observable Markov Decision Process (POMDP) provides a principled and generic framework to model real world sequential decision making processes but yet remains unsolved, especially for high dimensional continuous space and unknown models.

Decision Making Variational Inference

Feasible Actor-Critic: Constrained Reinforcement Learning for Ensuring Statewise Safety

1 code implementation22 May 2021 Haitong Ma, Yang Guan, Shegnbo Eben Li, Xiangteng Zhang, Sifa Zheng, Jianyu Chen

The safety constraints commonly used by existing safe reinforcement learning (RL) methods are defined only on expectation of initial states, but allow each certain state to be unsafe, which is unsatisfying for real-world safety-critical tasks.

reinforcement-learning Safe Exploration +1

Model-based Constrained Reinforcement Learning using Generalized Control Barrier Function

1 code implementation2 Mar 2021 Haitong Ma, Jianyu Chen, Shengbo Eben Li, Ziyu Lin, Yang Guan, Yangang Ren, Sifa Zheng

Model information can be used to predict future trajectories, so it has huge potential to avoid dangerous region when implementing reinforcement learning (RL) on real-world tasks, like autonomous driving.

Autonomous Driving reinforcement-learning +1

Mixed Policy Gradient

2 code implementations23 Feb 2021 Yang Guan, Jingliang Duan, Shengbo Eben Li, Jie Li, Jianyu Chen, Bo Cheng

MPG contains two types of PG: 1) data-driven PG, which is obtained by directly calculating the derivative of the learned Q-value function with respect to actions, and 2) model-driven PG, which is calculated using BPTT based on the model-predictive return.

Decision Making

Separated Proportional-Integral Lagrangian for Chance Constrained Reinforcement Learning

no code implementations17 Feb 2021 Baiyu Peng, Yao Mu, Jingliang Duan, Yang Guan, Shengbo Eben Li, Jianyu Chen

Taking a control perspective, we first interpret the penalty method and the Lagrangian method as proportional feedback and integral feedback control, respectively.

Autonomous Driving reinforcement-learning

Steadily Learn to Drive with Virtual Memory

no code implementations16 Feb 2021 Yuhang Zhang, Yao Mu, Yujie Yang, Yang Guan, Shengbo Eben Li, Qi Sun, Jianyu Chen

Reinforcement learning has shown great potential in developing high-level autonomous driving.

Autonomous Driving

A Safe Hierarchical Planning Framework for Complex Driving Scenarios based on Reinforcement Learning

no code implementations17 Jan 2021 Jinning Li, Liting Sun, Jianyu Chen, Masayoshi Tomizuka, Wei Zhan

To address this challenge, we propose a hierarchical behavior planning framework with a set of low-level safe controllers and a high-level reinforcement learning algorithm (H-CtRL) as a coordinator for the low-level controllers.

Autonomous Vehicles reinforcement-learning

Model-Based Actor-Critic with Chance Constraint for Stochastic System

no code implementations19 Dec 2020 Baiyu Peng, Yao Mu, Yang Guan, Shengbo Eben Li, Yuming Yin, Jianyu Chen

Safety is essential for reinforcement learning (RL) applied in real-world situations.

Efficient Deep Learning of Non-local Features for Hyperspectral Image Classification

1 code implementation2 Aug 2020 Yu Shen, Sijie Zhu, Chen Chen, Qian Du, Liang Xiao, Jianyu Chen, Delu Pan

Therefore, to incorporate the long-range contextual information, a deep fully convolutional network (FCN) with an efficient non-local module, named ENL-FCN, is proposed for HSI classification.

General Classification Hyperspectral Image Classification

The Design and Implementation of a Real Time Visual Search System on JD E-commerce Platform

1 code implementation19 Aug 2019 Jie Li, Haifeng Liu, Chuanghua Gui, Jianyu Chen, Zhenyun Ni, Ning Wang

We present the design and implementation of a visual search system for real time image retrieval on JD. com, the world's third largest and China's largest e-commerce site.

Image Retrieval

Intention-aware Long Horizon Trajectory Prediction of Surrounding Vehicles using Dual LSTM Networks

no code implementations6 Jun 2019 Long Xin, Pin Wang, Ching-Yao Chan, Jianyu Chen, Shengbo Eben Li, Bo Cheng

As autonomous vehicles (AVs) need to interact with other road users, it is of importance to comprehensively understand the dynamic traffic environment, especially the future possible trajectories of surrounding vehicles.

Autonomous Vehicles Intent Detection +1

Model-free Deep Reinforcement Learning for Urban Autonomous Driving

1 code implementation20 Apr 2019 Jianyu Chen, Bodi Yuan, Masayoshi Tomizuka

Urban autonomous driving decision making is challenging due to complex road geometry and multi-agent interactions.

Autonomous Driving Decision Making +1

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