Name: Zehong (Jimmy) Cao
Type: User
Company: University of South Australia
Bio: A passionate researcher in humans and artificial intelligence, brain-computer interface, and interactive machine learning.
Location: Adelaide, Australia
Blog: czh513.github.io
Zehong (Jimmy) Cao's Projects
Reproduction of OpenAI and DeepMind's "Deep Reinforcement Learning from Human Preferences"
Code for Deep RL from Human Preferences [Christiano et al]. Plus a webapp for collecting human feedback
Demonstration of Hierarchical and Non-Hierarchical Multi-Agent Interactions Based on Unity Reinforcement Learning
Reinforcement Learning Algorithms:SAC, TD3, TAC
Personalized Training for the Sequence Learning task with the NAO robot and the MUSE EEG sensor
Self-Organizing Fuzzy Neural Network
An educational resource to help anyone learn deep reinforcement learning.
Implementations of QMIX, VDN, COMA, QTRAN, MAVEN, CommNet, DyMA-CL, and G2ANet on SMAC, the decentralised micromanagement scenario of StarCraft II
Code for "Learning to summarize from human feedback"
Proximal Policy Optimization (PPO) algorithm for Super Mario Bros
Text preprocessing, representation and visualization from zero to hero.
An elegant, flexible, and superfast PyTorch deep Reinforcement Learning platform.
Transfer-Learning-Library
Unity Simulation Coronavirus Example
Unity Machine Learning Agents Toolkit
Webots Robot Simulator
More than the default behavior of a GA device, the robot, with this project can run custom Choregraphe projects, by saying something like "execute object recognition".
PyTorch package for the discrete VAE used for DALL·E.