149 citations · 360 across the 29 of their papers we have counts for
30 papers
Adaptive Approximation-Based Control for Nonlinear Systems: A Unified Solution with Accurate and Inaccurate Measurements
Dong Zhao
A unified solution to adaptive approximation-based control for nonlinear systems with accurate and inaccurate state measurement is synthesized in this study. Starting from the stan…
Score-Based Equilibrium Learning in Multi-Player Finite Games with Imperfect Information
Runyu Lu, Yuanheng Zhu, Dongbin Zhao
Real-world games, which concern imperfect information, multiple players, and simultaneous moves, are less frequently discussed in the existing literature of game theory. While rein…
Planning-inspired Hierarchical Trajectory Prediction for Autonomous Driving
Ding Li, Qichao Zhang, Zhongpu Xia +4
Recently, anchor-based trajectory prediction methods have shown promising performance, which directly selects a final set of anchors as future intents in the spatio-temporal couple…
NeuronsMAE: A Novel Multi-Agent Reinforcement Learning Environment for Cooperative and Competitive Multi-Robot Tasks
Guangzheng Hu, Haoran Li, Shasha Liu +3
Multi-agent reinforcement learning (MARL) has achieved remarkable success in various challenging problems. Meanwhile, more and more benchmarks have emerged and provided some standa…
A Hierarchical Deep Reinforcement Learning Framework for 6-DOF UCAV Air-to-Air Combat
Jiajun Chai, Wenzhang Chen, Yuanheng Zhu +2
Unmanned combat air vehicle (UCAV) combat is a challenging scenario with continuous action space. In this paper, we propose a general hierarchical framework to resolve the within-v…
Prototypical context-aware dynamics generalization for high-dimensional model-based reinforcement learning
Junjie Wang, Yao Mu, Dong Li +6
The latent world model provides a promising way to learn policies in a compact latent space for tasks with high-dimensional observations, however, its generalization across diverse…
Conditional Goal-oriented Trajectory Prediction for Interacting Vehicles with Vectorized Representation
Ding Li, Qichao Zhang, Shuai Lu +2
This paper aims to tackle the interactive behavior prediction task, and proposes a novel Conditional Goal-oriented Trajectory Prediction (CGTP) framework to jointly generate scene-…
NVIF: Neighboring Variational Information Flow for Large-Scale Cooperative Multi-Agent Scenarios
Jiajun Chai, Yuanheng Zhu, Dongbin Zhao
Communication-based multi-agent reinforcement learning (MARL) provides information exchange between agents, which promotes the cooperation. However, existing methods cannot perform…
TrajGen: Generating Realistic and Diverse Trajectories with Reactive and Feasible Agent Behaviors for Autonomous Driving
Qichao Zhang, Yinfeng Gao, Yikang Zhang +5
Realistic and diverse simulation scenarios with reactive and feasible agent behaviors can be used for validation and verification of self-driving system performance without relying…
UNMAS: Multi-Agent Reinforcement Learning for Unshaped Cooperative Scenarios
Jiajun Chai, Weifan Li, Yuanheng Zhu +4
Multi-agent reinforcement learning methods such as VDN, QMIX, and QTRAN that adopt centralized training with decentralized execution (CTDE) framework have shown promising results i…
Multi-task Safe Reinforcement Learning for Navigating Intersections in Dense Traffic
Yuqi Liu, Qichao Zhang, Dongbin Zhao
Multi-task intersection navigation including the unprotected turning left, turning right, and going straight in dense traffic is still a challenging task for autonomous driving. Fo…
Empirical Policy Optimization for -Player Markov Games
Yuanheng Zhu, Dongbin Zhao, Mengchen Zhao +1
In single-agent Markov decision processes, an agent can optimize its policy based on the interaction with environment. In multi-player Markov games (MGs), however, the interaction…
A Reinforcement Learning Benchmark for Autonomous Driving in Intersection Scenarios
Yuqi Liu, Qichao Zhang, Dongbin Zhao
In recent years, control under urban intersection scenarios becomes an emerging research topic. In such scenarios, the autonomous vehicle confronts complicated situations since it…
Benchmarking Lane-changing Decision-making for Deep Reinforcement Learning
Junjie Wang, Qichao Zhang, Dongbin Zhao
The development of autonomous driving has attracted extensive attention in recent years, and it is essential to evaluate the performance of autonomous driving. However, testing on…
Event-Triggered Multi-agent Reinforcement Learning with Communication under Limited-bandwidth Constraint
Guangzheng Hu, Yuanheng Zhu, Dongbin Zhao +2
Communicating with each other in a distributed manner and behaving as a group are essential in multi-agent reinforcement learning. However, real-world multi-agent systems suffer fr…
Dynamic Horizon Value Estimation for Model-based Reinforcement Learning
Junjie Wang, Qichao Zhang, Dongbin Zhao +2
Existing model-based value expansion methods typically leverage a world model for value estimation with a fixed rollout horizon to assist policy learning. However, the fixed rollou…
Heuristic Rank Selection with Progressively Searching Tensor Ring Network
Nannan Li, Yu Pan, Yaran Chen +3
Recently, Tensor Ring Networks (TRNs) have been applied in deep networks, achieving remarkable successes in compression ratio and accuracy. Although highly related to the performan…
BNAS-v2: Memory-efficient and Performance-collapse-prevented Broad Neural Architecture Search
Zixiang Ding, Yaran Chen, Nannan Li +1
In this paper, we propose BNAS-v2 to further improve the efficiency of NAS, embodying both superiorities of BCNN simultaneously. To mitigate the unfair training issue of BNAS, we e…
Deep Reinforcement Learning based Automatic Exploration for Navigation in Unknown Environment
Haoran Li, Qichao Zhang, Dongbin Zhao
This paper investigates the automatic exploration problem under the unknown environment, which is the key point of applying the robotic system to some social tasks. The solution to…
ContourRend: A Segmentation Method for Improving Contours by Rendering
Junwen Chen, Yi Lu, Yaran Chen +2
A good object segmentation should contain clear contours and complete regions. However, mask-based segmentation can not handle contour features well on a coarse prediction grid, th…
BiFNet: Bidirectional Fusion Network for Road Segmentation
Haoran Li, Yaran Chen, Qichao Zhang +1
Multi-sensor fusion-based road segmentation plays an important role in the intelligent driving system since it provides a drivable area. The existing mainstream fusion method is ma…
ModuleNet: Knowledge-inherited Neural Architecture Search
Yaran Chen, Ruiyuan Gao, Fenggang Liu +1
Although Neural Architecture Search (NAS) can bring improvement to deep models, they always neglect precious knowledge of existing models. The computation and time costing property…
Enhanced Rolling Horizon Evolution Algorithm with Opponent Model Learning: Results for the Fighting Game AI Competition
Zhentao Tang, Yuanheng Zhu, Dongbin Zhao +1
The Fighting Game AI Competition (FTGAIC) provides a challenging benchmark for 2-player video game AI. The challenge arises from the large action space, diverse styles of character…
Graph-FCN for image semantic segmentation
Yi Lu, Yaran Chen, Dongbin Zhao +1
Semantic segmentation with deep learning has achieved great progress in classifying the pixels in the image. However, the local location information is usually ignored in the high-…