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100-days-of-ml-code-chinese-version

Chinese Translation for Machine Learning Infographics

cpp-crash-course

C++ Crash Course

ddpg

Reimplementation of DDPG(Continuous Control with Deep Reinforcement Learning) based on OpenAI Gym + Tensorflow

deeplearning-500-questions

深度学习500问，以问答形式对常用的概率知识、线性代数、机器学习、深度学习、计算机视觉等热点问题进行阐述，以帮助自己及有需要的读者。 全书分为15个章节，近20万字。由于水平有限，书中不妥之处恳请广大读者批评指正。 未完待续............ 如有意合作，联系[email protected] 版权所有，违权必究 Tan 2018.06

dlinterview

Deep Learning Interview 深度学习面试题目汇总

dqn-obstacle-avoidance

Deep Reinforcement Learning for Fixed-Wing Flight Control with Deep Q-Network

dynamicvoronoi

dynamic_voronoi library

gym_ped_sim

A ros gazebo plugin for pedestrians （Raw depth social compliant navigation through GAIL) ICRA 2018

huawei2019-with-visualization

华为2019评判器开源（带可视化哟~）

infogail

[NIPS 2017] InfoGAIL: Interpretable Imitation Learning from Visual Demonstrations

interview_python

关于Python的面试题

inverse-reinforcement-learning

Implementations of selected inverse reinforcement learning algorithms.

jetson-reinforcement

Deep reinforcement learning GPU libraries for NVIDIA Jetson with PyTorch, OpenAI Gym, and Gazebo robotics simulator.

kaggle

linux

Linux kernel source tree

machine-learning

:zap:机器学习实战（Python3）：kNN、决策树、贝叶斯、逻辑回归、SVM、线性回归、树回归

machine-learning-in-action-python3

Data&code for Machine-Learning-in-Action by Python3 | 《机器学习实战》数据与Python3源码:cat2:

obstacleavoidancewithq-learning

Obstacle Avoidance Bot training on V-rep using Q learning Algorithm. Neural Networks were used as function approximator for state space

openbilibili

哔哩哔哩后台源码

reinforcement-learning-with-tensorflow

Simple Reinforcement learning tutorials

reinforcement_learning_in_python

Implementing Reinforcement Learning, namely Q-learning and Sarsa algorithms, for global path planning of mobile robot in unknown environment with obstacles. Comparison analysis of Q-learning and Sarsa

reinforcment-learning-with-q-learning

temporal_difference_learning_path_planning

When born, animals and humans are thrown into an unknown world forced to use their sensory inputs for survival. As they begin to understand and develop their senses they are able to navigate and interact with their environment. The process in which we learn to do this is called reinforcement learning. This is the idea that learning comes from a series of trial and error where there exists rewards and punishments for every action. The brain naturally logs these events as experiences, and decides new actions based on past experience. An action resulting in a reward will then be higher favored than an action resulting in a punishment. Using this concept, autonomous systems, such as robots, can learn about their environment in the same way. Using simulated sensory data from ultrasonic sensors, moisture sensors, encoders, shock sensors, pressure sensors, and steepness sensors, a robotic system will be able to make decisions on how to navigate through its environment to reach a goal. The robotic system will not know the source of the data or the terrain it is navigating. Given a map of an open environment simulating an area after a natural disaster, the robot will use model-free temporal difference learning with exploration to find the best path to a goal in terms of distance, safety, and terrain navigation. Two forms of temporal difference learning will be tested; off-policy (Q-Learning) and onpolicy (Sarsa). Through experimentation with several world map sizes, it is found that the off-policy algorithm, Q-Learning, is the most reliable and efficient in terms of navigating a known map with unequal states.

tongjithesis

同济大学硕博士论文LaTeX模板

voronoi_planner

ROS navigation stack base global planner plugin

write-a-c-interpreter

Write a simple interpreter of C. Inspired by c4 and largely based on it.

xx-net

a web proxy tool