shitianyu-hue Goto Github PK

academic-kickstart

agebias

process for age bias dataset

ageism-research

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

agenttuning

AgentTuning: Enabling Generalized Agent Abilities for LLMs

ai-follow

梳理每周最新多模态，LLMs,embodied AI相关论文

algorithmic-thinking

Part of the Fundamentals of Computing Specialization, Rice, Coursera

algorithms

argoverse-api

Official GitHub repository for Argoverse dataset

asl-recognition

astgcn

Attention Based Spatial-Temporal Graph Convolutional Networks for Traffic Flow Forecasting (ASTGCN) AAAI 2019

atari-agents

Code and links for trained Atari agents

awesome-python

A curated list of awesome Python frameworks, libraries, software and resources

bcq

Author's PyTorch implementation of BCQ for continuous and discrete actions

challenge-aido_rl-il

Reinforcement Learning + Imitation Learning based approach to AI Driving Olympics

chatbot_rlhf

chatglm-6b

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

citylearn

Official reinforcement learning environment for demand response and load shaping

coiltraine

Training framework for conditional imitation learning

comp-767-project

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

comp551-review

coursera-algorithms-part1

📖Coursera Princeton Algorithms Part 1

coursera-capstone-project_ibm

coursera-data-mining

Data Mining - University of Illinois at Urbana-Champaign

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.

deeprl_network

multi-agent deep reinforcement learning for networked system control.

densetnt

dgn

DGN Code

dgn_ring_torch

dicg

Deep Implicit Coordination Graphs

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.