Various Control Barrier Functions realized on cartpole.
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Updated
Jun 29, 2024 - Python
Various Control Barrier Functions realized on cartpole.
PyBullet CartPole and Quadrotor environments—with CasADi symbolic a priori dynamics—for learning-based control and RL
Implementing DeepQNetwork and Q learning on gymnasium CartPole-V1 env.
High-fidelity cartpole environment for reinforcement learning
CartPole game by Reinforcement Learning, a journey from training to inference
Optimal control solver implemented in Python. SymPy for symbolic differentiation and Numba for fast computation.
Implementation of Double DQN reinforcement learning for OpenAI Gym environments with PyTorch.
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)
Behaviour Cloning of Cartpole Swing-up Policy with Model-Predictive Uncertainty Regularization (UW CSE571 Guided Project)
Code for FLEX, a fast, adaptive and flexible model-based reinforcement learning exploration algorithm.
Comparing VPG, TRPO and PPO from Policy Gradient family
Applying DeepMind's MuZero algorithm to the cart pole environment in gym
NeurIPS 2019: DQN(λ) = Deep Q-Network + λ-returns.
My DRL(Deep Reinforcement Learning ) algorithm demo, base on pytorch and gym environment.
OpenAI's cartpole env solver.
Deep RL on OpenAI gym environment
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