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This repository is for an open-source environment for multi-agent active voltage control on power distribution networks (MAPDN).
JMLR: OmniSafe is an infrastructural framework for accelerating SafeRL research.
NeurIPS 2023: Safety-Gymnasium: A Unified Safe Reinforcement Learning Benchmark
NeurIPS 2023: Safe Policy Optimization: A benchmark repository for safe reinforcement learning algorithms
🚀 A fast safe reinforcement learning library in PyTorch
The repository is for safe reinforcement learning baselines.
DRLib：a Concise Deep Reinforcement Learning Library, Integrating HER, PER and D2SR for Almost Off-Policy RL Algorithms.
Implementation of Algorithms from the Policy Gradient Family. Currently includes: A2C, A3C, DDPG, TD3, SAC
The continuous mountain car problem solved with DDPG
主要利用QLearning,DQN,ImprovedDQN(Ddouble DQN) 解决gym框架下的三个问题CartPole-v0,MountainCar-v0,Acrobot-v1
Neural Network Dynamics for Model-Based Deep Reinforcement Learning with Model-Free Fine-Tuning
Study control engineering (mainly optimal control and model predictive control) and reinforcement learning using toy problem.
This repository demonstrates the reinforcement learning TD Q-Learning algorithm to control the level of the tank
Reinforcement Learning for Continues action and space states
using reinforcement learning controls dynamic continuous system
This repository contains PyTorch implementations of deep reinforcement learning algorithms and environments for Robotics and Controls. The goal of this project is to include engineering application…
ADP demo code for Reinforcement Learning and Control, Tsinghua Univ. Lecture Notes.
Master thesis spring 2019. Template to be futher used by the department of chemical engineering at NTNU,
Pytorch version of the MPC in model-based reinforcement learning (MBRL), currently only test in the CartPole-swing-up environment
Unofficial Implementation of the paper "Data-Efficient Reinforcement Learning with Probabilistic Model Predictive Control", applied to gym environments
PID controller and Reinforcement Learning approach to control a self-balancing robot (segway)
References on Optimal Control, Reinforcement Learning and Motion Planning
PILCO policy search framework (Matlab version)
PyTorch implementation of DQN, AC, ACER, A2C, A3C, PG, DDPG, TRPO, PPO, SAC, TD3 and ....
A clean and robust implementation of Duel Double DQN
Clean, Robust, and Unified PyTorch implementation of popular Deep Reinforcement Learning (DRL) algorithms (Q-learning, Duel DDQN, PER, C51, Noisy DQN, PPO, DDPG, TD3, SAC, ASL)