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The repository is for safe reinforcement learning baselines.
This submission contains a model to show the implementation of MPC on a vehicle moving in a US Highway scene.
A curated list of resources for using LLMs to develop more competitive grant applications.
NeurIPS 2023: Safety-Gymnasium: A Unified Safe Reinforcement Learning Benchmark
PyBullet CartPole and Quadrotor environments—with CasADi symbolic a priori dynamics—for learning-based control and RL
Clean, Robust, and Unified PyTorch implementation of popular Deep Reinforcement Learning (DRL) algorithms (Q-learning, Duel DDQN, PER, C51, Noisy DQN, PPO, DDPG, TD3, SAC, ASL)
CDC2024_submission_repository
a learning-based decision-making algorithm for on-ramp merging
we combine safe reinforcement learning with MPC to enhance the safety in the on-ramp merging scenario
Safe control of unknown dynamic systems with reinforcement learning and model predictive control
A fast and differentiable model predictive control (MPC) solver for PyTorch.
Safety-aware MPC-based RL framework
Reinforcement Learning with Model Predictive Control
Pytorch version of the MPC in model-based reinforcement learning (MBRL), currently only test in the CartPole-swing-up environment
This is example code of Adaptive cruise control (ACC) via control Lyapunov function and CBF
"Safety-Critical Model Predictive Control with Discrete-Time Control Barrier Function" by J. Zeng, B. Zhang and K. Sreenath https://arxiv.org/abs/2007.11718
A collection of work using nonlinear model predictive control (NMPC) with discrete-time control Lyapunov functions (CLFs) and control barrier functions (CBFs)
Model-Free Safe Reinforcement Learning through Neural Barrier Certificate
OpenDILab Decision AI Engine. The Most Comprehensive Reinforcement Learning Framework B.P.
This is the homepage of a new book entitled "Mathematical Foundations of Reinforcement Learning."
An elegant PyTorch deep reinforcement learning library.
A minimalist environment for decision-making in autonomous driving