Michael Lu's repositories
ciff
Cornell Instruction Following Framework
cleanrl
High-quality single file implementation of Deep Reinforcement Learning algorithms with research-friendly features
cogail
Co-GAIL: Learning Diverse Strategies for Human-Robot Collaboration
cpo-pytorch
An implementation of Constrained Policy Optimization (Achiam 2017) in PyTorch
event-jekyll-theme
Jekyll Theme package for your event
gail-airl-ppo.pytorch
A PyTorch implementation of GAIL and AIRL based on PPO.
jetRacerROSpkg
A ROS implementation of the jetRacer control platform running on an NVIDIA Jetson Nano
omnisafe
OmniSafe is an infrastructural framework for accelerating SafeRL research.
optimized_dp
Optimizing Dynamic Programming-Based Algorithms
PyTorch-RL
PyTorch implementation of Deep Reinforcement Learning: Policy Gradient methods (TRPO, PPO, A2C) and Generative Adversarial Imitation Learning (GAIL). Fast Fisher vector product TRPO.
pytorch-soft-actor-critic
PyTorch implementation of soft actor critic
recovery-rl
Implementation of Recovery RL: Safe Reinforcement Learning With Learned Recovery Zones.
rl-baselines3-zoo
A training framework for Stable Baselines3 reinforcement learning agents, with hyperparameter optimization and pre-trained agents included.
rl-starter-files
RL starter files in order to immediatly train, visualize and evaluate an agent without writing any line of code
safe-control-gym
PyBullet CartPole and Quadrotor environments—with CasADi symbolic a priori dynamics—for learning-based control and reinforcement learning
Safe-MBPO
Code for the NeurIPS 2021 paper "Safe Reinforcement Learning by Imagining the Near Future"
safety-gym
Tools for accelerating safe exploration research.
safety-gymnasium
Safety-Gymnaisum is a highly scalable and customizable safe reinforcement learning environment library.
siren-jax
Unofficial implementation of Siren with Jax for image representation.
spinningup
An educational resource to help anyone learn deep reinforcement learning.
stable-baselines3
PyTorch version of Stable Baselines, reliable implementations of reinforcement learning algorithms.