YL03's Stars
johannes-manner/SeMoDe
SeMoDe is a tool to support lifecycle activites of Serverless functions on different platforms. Currently automated test generation on AWS Lambda is possible and performance considerations due to the cold start issue are work in progress.
Azure-Samples/functions-distributed-tracing-sample
Distributed Tracing sample for Azure Functions and Java with Application Insights
kaixindelele/DRLib
DRLib：a Concise Deep Reinforcement Learning Library, Integrating HER, PER and D2SR for Almost Off-Policy RL Algorithms.
ZYunfeii/DRL_algorithm_library
This is a reinforcement learning algorithm library. The code takes into account both performance and simplicity, with little dependence.
revenol/DROO
Deep Reinforcement Learning for Online Computation Offloading in Wireless Powered Mobile-Edge Computing Networks
apourchot/CEM-RL
Combining Evolutionary Algorithms and deep RL in various ways
wyjung0625/p3s
Implementation of Population-Guided Parallel Policy Search for Reinforcement Learning
DLR-RM/rl-baselines3-zoo
A training framework for Stable Baselines3 reinforcement learning agents, with hyperparameter optimization and pre-trained agents included.
twni2016/Meta-SAC
Auto-tune the Entropy Temperature of Soft Actor-Critic via Metagradient - 7th ICML AutoML workshop 2020
DLR-RM/stable-baselines3
PyTorch version of Stable Baselines, reliable implementations of reinforcement learning algorithms.
ikostrikov/pytorch-a2c-ppo-acktr-gail
PyTorch implementation of Advantage Actor Critic (A2C), Proximal Policy Optimization (PPO), Scalable trust-region method for deep reinforcement learning using Kronecker-factored approximation (ACKTR) and Generative Adversarial Imitation Learning (GAIL).
nikhilbarhate99/PPO-PyTorch
Minimal implementation of clipped objective Proximal Policy Optimization (PPO) in PyTorch
facebookresearch/hydra
Hydra is a framework for elegantly configuring complex applications
p-christ/Deep-Reinforcement-Learning-Algorithms-with-PyTorch
PyTorch implementations of deep reinforcement learning algorithms and environments
facebookresearch/LearningToLearn
Collection of algorithms to learn loss and reward functions via gradient-based bi-level optimization.
eambutu/snail-pytorch
Implementation of "A Simple Neural Attentive Meta-Learner" (SNAIL, https://arxiv.org/pdf/1707.03141.pdf) in PyTorch
chanb/metalearning_RL
jxx123/rl-tf2
My own implementation of Reinforcement Learning algorithms using Tensorflow 2.0
quantumiracle/Benchmark-Efficient-Reinforcement-Learning-with-Demonstrations
Benchmark present methods for efficient reinforcement learning. Methods include Reptile, MAML, Residual Policy, etc. RL algorithms include DDPG, PPO.
ray-project/ray
Ray is a unified framework for scaling AI and Python applications. Ray consists of a core distributed runtime and a set of AI Libraries for accelerating ML workloads.
hill-a/stable-baselines
A fork of OpenAI Baselines, implementations of reinforcement learning algorithms
google-research/google-research
Google Research
tristandeleu/pytorch-maml-rl
Reinforcement Learning with Model-Agnostic Meta-Learning in Pytorch
amazon-science/meta-q-learning
Code for the paper "Meta-Q-Learning"( ICLR 2020)
toshikwa/sac-discrete.pytorch
PyTorch implementation of SAC-Discrete.
quantumiracle/Popular-RL-Algorithms
PyTorch implementation of Soft Actor-Critic (SAC), Twin Delayed DDPG (TD3), Actor-Critic (AC/A2C), Proximal Policy Optimization (PPO), QT-Opt, PointNet..
dragen1860/MAML-Pytorch
Elegant PyTorch implementation of paper Model-Agnostic Meta-Learning (MAML)
HaiyinPiao/pytorch-a2clstm-DRQN
using recurrent networks(LSTM) to solve POMDPs
Farama-Foundation/Metaworld
Collections of robotics environments geared towards benchmarking multi-task and meta reinforcement learning
AI4Finance-Foundation/ElegantRL
Massively Parallel Deep Reinforcement Learning. 🔥