This is the homepage of a new book entitled "Mathematical Foundations of Reinforcement Learning."

MATLAB example on how to use Reinforcement Learning for developing a financial trading model

Adaptive dynamic programming

出版书籍《机器学习入门到实践——MATLAB实践应用》一书中的实例程序。涉及监督学习，非监督学习和强化学习。(code for book "Machine Learning Introduction & action in MATLAB")

This repository showcases a hybrid control system combining Reinforcement Learning (Q-Learning) and Neural-Fuzzy Systems to dynamically tune a PID controller for an Autonomous Underwater Vehicle (AUV). The implementation aims to enhance precision, adaptability, and robustness in underwater environments.

Implementing a Reinforcement Learning algorithm based upon a partially observable Markov decision process, applicable in modelling decision making.

Develop agent-based traffic management system by model-free reinforcement learning

Train a reinforcement learning agent to play a variation of Pong®

We explore the application of deep reinforcement learning in the field of robotic control, the cooperative and competitive behavior of multi-agents in different game types, including RPG and MOBA, cloud infrastructure, and software engineering as well.

TD-Regularized Actor-Critic Methods

reinforcement learning for power grid optimal operations and maintenance

We use reachability to ensure the safety of a decision agent acting on a dynamic system in real-time. We compute the Forward Reachable Set offline and use it online to adjust any potentially unsafe decisions that cause a collision with an obstacle.

Code accompanying the paper: Mattar, M. G., & Daw, N. D. (2018). Prioritized memory access explains planning and hippocampal replay. bioRxiv, 225664.

A Machine Learning Approach for Power Allocation in HetNets Considering QoS

This repository contains the Matlab code used to generate the results in the paper “Enhancement of a state-of-the-art RL-based detection algorithm for Massive MIMO radars” https://ieeexplore.ieee.org/abstract/document/9760145

Memristor Reinforcement Learning

Code for the paper {Pang, Bo, and Zhong-Ping Jiang. "Reinforcement Learning for Adaptive Optimal Stationary Control of Linear Stochastic Systems." arXiv preprint arXiv:2107.07788 (2021)."}

使用深度强化学习解决视觉跟踪和视觉导航问题

Advantage Actor-Critic (A2C) reinforcement learning agent used to control the motor speeds on a quadcopter in order to keep the quadcopter in a stable hover following a random angular acceleration perturbation between 0-3 degrees per second in each of the control axes: pitch, roll, and yaw.

Minimal Policy Search Toolbox