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This is the official code for CoRL 2024 work "Verification of Neural Control Barrier Functions with Symbolic Derivative Bounds Propagation".
Code of the paper "Feasible Policy Iteration".
This is the homepage of a new book entitled "Mathematical Foundations of Reinforcement Learning."
Code for Policy Bifurcation in Safe Reinforcement Learning
Code of the paper "Synthesizing Control Barrier Functions With Feasible Region Iteration for Safe Reinforcement Learning".
Code of the paper "The Feasibility of Constrained Reinforcement Learning Algorithms: A Tutorial Study".
Model-Free Safe Reinforcement Learning through Neural Barrier Certificate
Github Pages template for academic personal websites, forked from mmistakes/minimal-mistakes
General Optimal control Problem Solver (GOPS), an easy-to-use PyTorch reinforcement learning solver package for industrial control.
An API standard for single-agent reinforcement learning environments, with popular reference environments and related utilities (formerly Gym)
Official implementation of the paper: Safe Model-Based Reinforcement Learning with an Uncertainty-Aware Reachability Certificate
PyBullet CartPole and Quadrotor environments—with CasADi symbolic a priori dynamics—for learning-based control and RL
Basic constrained RL agents used in experiments for the "Benchmarking Safe Exploration in Deep Reinforcement Learning" paper.
Ray is an AI compute engine. Ray consists of a core distributed runtime and a set of AI Libraries for accelerating ML workloads.
An educational resource to help anyone learn deep reinforcement learning.
Paper list for constrained policy optimization in reinforcement learning.
Tools for accelerating safe exploration research.
A toolkit for developing and comparing reinforcement learning algorithms.
A minimalist environment for decision-making in autonomous driving
Dream to Control: Learning Behaviors by Latent Imagination
Dream to Control: Learning Behaviors by Latent Imagination, implemented in PyTorch.
Unofficial Re-implementation of "Dream to Control: Learning Behaviors by Latent Imagination" (https://arxiv.org/abs/1912.01603 ) with PyTorch
PyTorch implementation of Stochastic Latent Actor-Critic(SLAC).
Vanilla DQN, Double DQN, and Dueling DQN implemented in PyTorch
Excellent FastSLAM with 2D Laser Scan Match in Python Environment