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
Iterative Linear Quadratic Regulator with auto-differentiatiable dynamics models
Simple Cartpole example writed with pytorch.
Python implementation of MPPI (Model Predictive Path-Integral) controller to understand the basic idea. Mandatory dependencies are numpy and matplotlib only.
OpenAI's cartpole env solver.
강화학습에 대한 기본적인 알고리즘 구현
Reinforcing Your Learning of Reinforcement Learning
AutoDiff DAG constructor, built on numpy and Cython. A Neural Turing Machine and DeepQ agent run on it. Clean code for educational purpose.
Implementation of Double DQN reinforcement learning for OpenAI Gym environments with PyTorch.
Reinforcement Learning Agents in .NET
A toolbox for trajectory optimization of dynamical systems
Implementation and examples from Trajectory Optimization with Optimization-Based Dynamics https://arxiv.org/abs/2109.04928
This is a pip package implementing Reinforcement Learning algorithms in non-stationary environments supported by the OpenAI Gym toolkit.
CartPole game by Reinforcement Learning, a journey from training to inference
NeurIPS 2019: DQN(λ) = Deep Q-Network + λ-returns.
使用pytorch构建深度强化学习模型DQN
Various Control Barrier Functions realized on cartpole.
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