Concise pytorch implements of DRL algorithms, including REINFORCE, A2C, DQN, PPO(discrete and continuous), DDPG, TD3, SAC.

深度强化学习路径规划, SAC路径规划, Soft Actor-Critic算法, SAC-pytorch，激光雷达Lidar避障，激光雷达仿真，Adaptive-SAC

Deep Reinforcement Learning for mobile robot navigation in IR-SIM simulation. Using DRL (SAC, TD3, PPO, DDPG) neural networks, a robot learns to navigate to a random goal point in a simulated environment while avoiding obstacles.

控制算法，状态、输出反馈控制。ADRC自抗扰控制，抗积分饱和PID控制，增量式PID控制，模糊FuzzyPID控制，线性二次型调节器LQR控制，线性二次型积分器LQI控制，迭代iLQR控制，模型预测MPC控制，AI智能控制，启发算法控制，强化学习SAC、PPO控制，无人机、机器人、小车轨迹跟踪控制

A Torch Based RL Framework for Rapid Prototyping of Research Papers

DRL-Base-EMS for HEVs

Project of Reinforcement Learning course

A codebase for continuous action spaces Reinforcement Learning algorithms

A PyTorch implementation of the Soft Actor-Critic (SAC) algorithm to train an agent to play with the Humanoid environment from MuJoCo.

The pytorch implementation of sac

tabular and deep rl algorithms

PyTorch implementation of SAC (Soft Actor-Critic)

This repo aimed to reproducing of the results of Safety Representations for Safer Policy Learning paper in context of CENG 502 lecture in METU

Off-policy RL (DDPG, TD3, SAC) algorithms from scratch