32 projects in the framework of Deep Reinforcement Learning algorithms: Q-learning, DQN, PPO, DDPG, TD3, SAC, A2C and others. Each project is provided with a detailed training log.
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Updated
Jun 17, 2021 - Jupyter Notebook
32 projects in the framework of Deep Reinforcement Learning algorithms: Q-learning, DQN, PPO, DDPG, TD3, SAC, A2C and others. Each project is provided with a detailed training log.
PyBullet CartPole and Quadrotor environments—with CasADi symbolic a priori dynamics—for learning-based control and RL
OpenAI's cartpole env solver.
Iterative Linear Quadratic Regulator with auto-differentiatiable dynamics models
강화학습에 대한 기본적인 알고리즘 구현
Simple Cartpole example writed with pytorch.
Reinforcing Your Learning of Reinforcement Learning
Python implementation of MPPI (Model Predictive Path-Integral) controller to understand the basic idea. Mandatory dependencies are numpy and matplotlib only.
AutoDiff DAG constructor, built on numpy and Cython. A Neural Turing Machine and DeepQ agent run on it. Clean code for educational purpose.
A toolbox for trajectory optimization of dynamical systems
Implementation of Double DQN reinforcement learning for OpenAI Gym environments with PyTorch.
This is a pip package implementing Reinforcement Learning algorithms in non-stationary environments supported by the OpenAI Gym toolkit.
👾 My solutions to OpenAI Gym Reinforcement Learning problems.
A tutorial to learn RL from examples. This is my notes from a course of Baidu PaddlePaddle. (世界冠军带你从零实践强化学习)
NeurIPS 2019: DQN(λ) = Deep Q-Network + λ-returns.
PyTorch implementation of DQN
Run OpenAI Gym on a Server
使用pytorch构建深度强化学习模型DQN
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