OmniSafe is an infrastructural framework for accelerating SafeRL research.

🎹 🤖 A benchmark for high-dimensional robot control.

SBX: Stable Baselines Jax (SB3 + Jax)

Codebase for NeurIPS 2022 paper, "When to Ask for Help: Proactive Interventions in Autonomous Reinforcement Learning"

Safety-Gymnaisum is a highly scalable and customizable safe reinforcement learning environment library.

Unofficial implementation of Siren with Jax for image representation.

Implementation of Recovery RL: Safe Reinforcement Learning With Learned Recovery Zones.

High-quality single file implementation of Deep Reinforcement Learning algorithms with research-friendly features

Optimizing Dynamic Programming-Based Algorithms

Code for the NeurIPS 2021 paper "Safe Reinforcement Learning by Imagining the Near Future"

Tools for accelerating safe exploration research.

An implementation of Constrained Policy Optimization (Achiam 2017) in PyTorch

A PyTorch implementation of GAIL and AIRL based on PPO.

A training framework for Stable Baselines3 reinforcement learning agents, with hyperparameter optimization and pre-trained agents included.

PyTorch version of Stable Baselines, reliable implementations of reinforcement learning algorithms.

PyTorch implementation of soft actor critic

Co-GAIL: Learning Diverse Strategies for Human-Robot Collaboration

Cornell Instruction Following Framework

PyBullet CartPole and Quadrotor environments—with CasADi symbolic a priori dynamics—for learning-based control and reinforcement learning

RL starter files in order to immediatly train, visualize and evaluate an agent without writing any line of code

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