SaneLYX's repositories
ray
Ray is a unified framework for scaling AI and Python applications. Ray consists of a core distributed runtime and a toolkit of libraries (Ray AIR) for accelerating ML workloads.
MASA-QMIX
Paper《Solving job scheduling problems in a resource preemption environment with multi-agent reinforcement learning》
on-policy
Paper《The Surprising Effectiveness of PPO in Cooperative Multi-Agent Games》
pymarl
Python Multi-Agent Reinforcement Learning framework
Multi-Agent-Transformer
Paper《Multi-Agent Reinforcement Learning is a Sequence Modeling Problem》
TRPO-in-MARL
Paper《Trust Region Policy Optimisation in Multi-Agent Reinforcement Learning》
TSP_Att-GCRN-MCTS
Paper《Generalize a Small Pre-trained Model to Arbitrarily Large TSP Instances》
tensorflow
An Open Source Machine Learning Framework for Everyone
or-tools
Google's Operations Research tools:
keras
Deep Learning for humans
easy-rl
强化学习中文教程（蘑菇书），在线阅读地址：https://datawhalechina.github.io/easy-rl/
MARL-Papers
Paper list of multi-agent reinforcement learning (MARL)
End-to-end-DRL-for-FJSP
Paper《A Multi-action Deep Reinforcement Learning Framework for Flexible Job-shop Scheduling Problem》
pumpkin-book
《机器学习》（西瓜书）公式详解
ElegantRL
Cloud-native Deep Reinforcement Learning. 🔥
Deep-Reinforcement-Learning-Algorithms-with-PyTorch
PyTorch implementations of deep reinforcement learning algorithms and environments
d2l-zh
《动手学深度学习》：面向中文读者、能运行、可讨论。中英文版被60个国家的400所大学用于教学。
lihang-code
《统计学习方法》的代码实现
MARL-Algorithms
Implementations of IQL, QMIX, VDN, COMA, QTRAN, MAVEN, CommNet, DyMA-CL, and G2ANet on SMAC, the decentralised micromanagement scenario of StarCraft II
L2D
Paper《Learning to Dispatch for Job Shop Scheduling via Deep Reinforcement Learning》
tianshou
An elegant PyTorch deep reinforcement learning library.
nndl.github.io
《神经网络与深度学习》 邱锡鹏著 Neural Network and Deep Learning
RoutingProblemGANN
Paper《Solve routing problems with a residual edge-graph attention neural network》
DRL-code-pytorch
Concise pytorch implements of DRL algorithms, including REINFORCE, A2C, DQN, PPO(discrete and continuous), DDPG, TD3, SAC.
Deep-reinforcement-learning-with-pytorch
PyTorch implementation of DQN, AC, ACER, A2C, A3C, PG, DDPG, TRPO, PPO, SAC, TD3 and ....
Reinforcement-Implementation
Implementation of benchmark RL algorithms
fjsp-drl
Paper《Flexible Job Shop Scheduling via Graph Neural Network and Deep Reinforcement Learning》
scikit-opt
Genetic Algorithm, Particle Swarm Optimization, Simulated Annealing, Ant Colony Optimization Algorithm,Immune Algorithm, Artificial Fish Swarm Algorithm, Differential Evolution and TSP(Traveling salesman)
noisy-mappo
Multi-agent PPO with noise (97% win rates on Hard scenarios of SMAC)
baselines
OpenAI Baselines: high-quality implementations of reinforcement learning algorithms