JingdiC's Stars
transys-project/metis
Interpreting Deep Learning-Based Networking Systems (SIGCOMM 2020)
MattChanTK/gym-maze
A basic 2D maze environment where an agent start from the top left corner and try to find its way to the bottom left corner.
TonghanWang/NDQ
Codes accompanying the paper "Learning Nearly Decomposable Value Functions with Communication Minimization" (ICLR 2020)
nulziiorsh/KNN-locality-preserving-hashing
liuqiangus/DeepSlicing
Source Codes for: DeepSlicing: Deep Reinforcement Learning Assisted Resource Allocation for Network Slicing
PKU-RL/I2C
MJeremy2017/reinforcement-learning-implementation
Reinforcement Learning examples implementation and explanation
mueedhafiz1982/DQN-ensemble-for-RL
mohammadasghari/dqn-multi-agent-rl
Deep Q-learning (DQN) for Multi-agent Reinforcement Learning (RL)
Remtasya/Distributional-Multi-Agent-Actor-Critic-Reinforcement-Learning-MADDPG-Tennis-Environment
The state-of-the-art in multi-agent Reinforcement Learning is the MADDPG algorithm which utilises DDPG actor-critic neural networks where each agent uses centralized critic training but decentralized actor execution, and is capable of learning either cooperative or competitive environments. This is demonstrated on the Unity Tennis Environment.
caslab-vt/SARNet
Code repository for SARNet: Learning Multi-Agent Communication through Structured Attentive Reasoning (NeurIPS 2020)
murtazarang/MD-MADDPG
facebookarchive/CommNet
Neural network model, suitable for multi-agent learning. https://arxiv.org/abs/1605.07736
kclip/meta-autoencoder
Code for the paper "Meta-Learning to Communicate: Fast End-to-End Training for Fading Channels"