SHITIANYU-hue

agebias

process for age bias dataset

AI-follow

梳理每周最新多模态，LLMs,embodied AI相关论文

COMP-767-project

This is the source code of COMP 767 group project of Tianyu Shi & Jiawei Wang.

Data-driven-control

A reliable controller is critical for execution of safe and smooth maneuvers of an autonomous vehicle. The controller must be robust to external disturbances, such as road surface, weather, wind conditions, and so on. It also needs to deal with internal variations of vehicle sub-systems, including powertrain inefficiency, measurement errors, time delay, etc. These factors introduce issues in controller performance. In this paper, a feed-forward compensator is designed via a data-driven method to model and optimize the controller’s performance. Principal Component Analysis (PCA) is applied for extracting influential features, after which a Time Delay Neural Network is adopted to predict control errors over a future time horizon. Based on the predicted error, a feedforward compensator is then designed to improve control performance. Simulation results in different scenarios show that, with the help of with the proposed feedforward compensator, the maximum path tracking error and the steering wheel angle oscillation are improved by 44.4% and 26.7%, respectively.

DGN

DGN Code

dgn_ring_torch

DRL-robot-navigation

Deep Reinforcement Learning for mobile robot navigation in ROS Gazebo simulator. Using Twin Delayed Deep Deterministic Policy Gradient (TD3) neural network, a robot learns to navigate to a random goal point in a simulated environment while avoiding obstacles.

Efficient-motion-planning

To guarantee safe and efficient driving for automated vehicles in complicated traffic conditions, the motion planning module of automated vehicles are expected to generate collision-free driving policies as soon as possible in varying traffic environment. However, there always exist a tradeoff between efficiency and accuracy for the motion planning algorithms. Besides, most motion planning methods cannot find the desired trajectory under extreme scenarios (e.g., lane change in crowded traffic scenarios). This study proposed an efficient motion planning strategy for automated lane change based on Mixed-Integer Quadratic Optimization (MIQP) and Neural Networks. We modeled the lane change task as a mixed-integer quadratic optimization problem with logical constraints, which allows the planning module to generate feasible, safe and comfortable driving actions for lane changing process. Then, a hierarchical machine learning structure that consists of SVM-based classification layer and NN-based action learning layer is established to generate desired driving policies that can make online, fast and generalized motion planning. Our model is validated in crowded lane change scenarios through numerical simulations and results indicate that our model can provide optimal and efficient motion planning for automated vehicles

multiagent-particle-envs

Code for a multi-agent particle environment used in the paper "Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environments"

SUMO-changing-lane-agent

Implementation of a reinforcement learning agent able to do autonomous changing lane using Sumo

SHITIANYU-hue/AI-follow

梳理每周最新多模态，LLMs,embodied AI相关论文

SHITIANYU-hue/SUMO-changing-lane-agent

Implementation of a reinforcement learning agent able to do autonomous changing lane using Sumo

SHITIANYU-hue/agebias

process for age bias dataset

SHITIANYU-hue/DRL-robot-navigation

Deep Reinforcement Learning for mobile robot navigation in ROS Gazebo simulator. Using Twin Delayed Deep Deterministic Policy Gradient (TD3) neural network, a robot learns to navigate to a random goal point in a simulated environment while avoiding obstacles.

SHITIANYU-hue/sumosim

A sumo based simulator that can support both micro and macro level control

SHITIANYU-hue/interview-assistant

Load a PDF file and ask questions via llama_index and GPT

SHITIANYU-hue/SHITIANYU-hue.github.io

Github Pages template for academic personal websites, forked from mmistakes/minimal-mistakes

SHITIANYU-hue/Traffic-Speed-Control-System

SHITIANYU-hue/ageism-research

[ICML 2022] RankSim: Ranking Similarity Regularization for Deep Imbalanced Regression

SHITIANYU-hue/AgentTuning

AgentTuning: Enabling Generalized Agent Abilities for LLMs

SHITIANYU-hue/ASL-Recognition

SHITIANYU-hue/chatbot_rlhf

SHITIANYU-hue/ChatGLM-6B

ChatGLM-6B: An Open Bilingual Dialogue Language Model | 开源双语对话语言模型

SHITIANYU-hue/CityLearn

Official reinforcement learning environment for demand response and load shaping

SHITIANYU-hue/fairllm

SHITIANYU-hue/FairLLM-1

SHITIANYU-hue/glm

SHITIANYU-hue/graphalm--

SHITIANYU-hue/la-mbda

LAMBDA is a model-based reinforcement learning agent that uses Bayesian world models for safe policy optimization

SHITIANYU-hue/llmky

SHITIANYU-hue/nerfies.github.io

SHITIANYU-hue/optimization

SHITIANYU-hue/proximal-exploration

PyTorch implementation for our paper "Proximal Exploration for Model-guided Protein Sequence Design"

SHITIANYU-hue/pvp2

Official release for the code used in paper: Learning from Active Human Involvement through Proxy Value Propagation (NeurIPS 2023 Spotlight)

SHITIANYU-hue/RL_texas_holdem

SHITIANYU-hue/RLCFModel

Fine tune a pre-trained LLM using compiler generated RL feedback

SHITIANYU-hue/SECRM2D-demo

SHITIANYU-hue/story-analysis

SHITIANYU-hue/SUMO-DVSL

A SUMO environment for differential varaible speed limits control

SHITIANYU-hue/TradingGovernor