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
tabular and deep rl algorithms
The pytorch implementation of sac
PyTorch implementation of SAC (Soft Actor-Critic)
A PyTorch implementation of the Soft Actor-Critic (SAC) algorithm to train an agent to play with the Humanoid environment from MuJoCo.
Off-policy RL (DDPG, TD3, SAC) algorithms from scratch
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