Zhouqianqian's repositories
apollo
An open autonomous driving platform
awesome-rl
Reinforcement learning resources curated
cpo
Constrained Policy Optimization
CS-Xmind-Note
计算机专业课（408）思维导图和笔记：计算机组成原理（第五版 王爱英），数据结构（王道），计算机网络（第七版 谢希仁），操作系统（第四版 汤小丹）
Deep-Learning-Papers-Reading-Roadmap
Deep Learning papers reading roadmap for anyone who are eager to learn this amazing tech!
human-rl
Code for human intervention reinforcement learning
learning-from-human-preferences
Reproduction of OpenAI and DeepMind's "Deep Reinforcement Learning from Human Preferences"
LeaveNoTrace
Leave No Trace is an algorithm for safe reinforcement learning.
notes
Resources to learn more about Machine Learning and Artificial Intelligence
Overcoming-exploration-from-demos
Implementation of the paper "Overcoming Exploration in Reinforcement Learning with Demonstrations" Nair et al. over the HER baselines from OpenAI
python
Show Me the Code Python version.
reinforcement-learning
Implementation of Reinforcement Learning Algorithms. Python, OpenAI Gym, Tensorflow. Exercises and Solutions to accompany Sutton's Book and David Silver's course.
rl_nav
This is the accompannying code for the paper "SLAM-Safe Planner: Preventing Monocular SLAM Failure using Reinforcement Learning" and "Data driven strategies for Active Monocular SLAM using Inverse Reinforcement Learning" To run the code, download this repository and a modified version of PTAM from https://github.com/souljaboy764/ethzasl_ptam/ to your catkin workspace and compile it. For running the agent on maps: In the turtlebot_gazebo.launch change the argument "world_file" to the corresponding map world file (map1.world, map2.world, map3.world, corridor.world or rooms.world) and set the corresponding initial positions in joystick.launch Open 4 new terminals Terminal 1: roslaunch rl_nav turtlebot_gazebo.launch Terminal 2: roslaunch ptam ptam.launch Terminal 3: roslaunch rl_nav joystick.launch Terminal 4: rosrun rviz rviz -d `rospack find rl_nav`/ptam.rviz Press the "start" button on the xbox joystick or publish a message of type "std_msgs/Empty" to /rl/init Once PTAM is initialized, give an intermediate point using the "2D Pose Estimate" button in rviz and give the goal location using "2D Nav Goal" For traning the agent, In the turtlebot_gazebo.launch change the argument "world_file" to training.world Open 3 new terminals Terminal 1: roslaunch rl_nav turtlebot_gazebo.launch Terminal 2: roslaunch ptam ptam.launch Terminal 3: roslaunch rl_nav train.launch Press the "start" button on the xbox joystick or publish a message of type "std_msgs/Empty" to /rl/init Once PTAM is initialized, press the "A" button on the xbox controller to start training. For testing the agent on steps to breakage, In the turtlebot_gazebo.launch change the argument "world_file" to training.world Open 3 new terminals Terminal 1: roslaunch rl_nav turtlebot_gazebo.launch Terminal 2: roslaunch ptam ptam.launch Terminal 3: roslaunch rl_nav test.launch Press the "start" button on the xbox joystick or publish a message of type "std_msgs/Empty" to /rl/init Once PTAM is initialized, press the "A" button on the xbox controller to start testing. For running the IRL agent, just change the weights in qMatData.txt to the weights in qMatData_SGD.txt and run any of the above. For training the IRL agent, run IRLAgent.py with the data from https://www.dropbox.com/s/qnp8rs92kbmqz1e/qTrain.txt?dl=0 in the same folder as IRLAgent.py, which will save the final Q values in qRegressor.pkl
ros-bridge
ROS bridge for CARLA Simulator
show-me-the-code
Python 练习册，每天一个小程序
spinningup
An educational resource to help anyone learn deep reinforcement learning.
Training-Neural-Networks-for-Event-Based-End-to-End-Robot-Control
TUM Master’s thesis: Steering a robot with an event-based vision sensor in a lane-keeping task using methods such as Deep Reinforcement Learning or Spiking Neural Networks.