PyBullet CartPole and Quadrotor environments—with CasADi symbolic a priori dynamics—for learning-based control and RL
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Updated
May 24, 2024 - Python
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.
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.
A toolbox for trajectory optimization of dynamical systems
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.
Various Control Barrier Functions realized on cartpole.
Run OpenAI Gym on a Server
Applying DeepMind's MuZero algorithm to the cart pole environment in gym
Programmatically Interpretable Reinforcement Learning
PyTorch implementation of DQN
An implementation of main reinforcement learning algorithms: solo-agent and ensembled versions.
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