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asd

auto-features-extraction-for-rl

Features Extraction using Autoencoders for Reinforcement Learning Tasks

compositionalkoopmanoperators

[ICLR 2020] Learning Compositional Koopman Operators for Model-Based Control

drl

Deep Reinforcement Learning

drl-code-pytorch

Concise pytorch implements of DRL algorithms, including REINFORCE, A2C, DQN, PPO(discrete and continuous), DDPG, TD3, SAC.

drl_gcn_cvrp

An implementation for CVRP problem with A3C+Attention mechanism and GCN

dynamic-mode-decompositions

Codes to run some Dynamic Mode Decompositions (DMD) algorithms on multiple time-series data with some prebuilt choices of observables and example simulation models in python modules. The one step and N step options refer only to the predictions using the dynamics matrix rather than its estimation itself. Used research at University of California Santa Barbara (UCSB).

flask

flow-1

Computational framework for reinforcement learning in traffic control

gcq_source

GCN CAV

graph_convolutional_lstm

Traffic Graph Convolutional Recurrent Neural Network

hierarchical_il_rl

Code for hierarchical imitation learning and reinforcement learning

keras-rl

Deep Reinforcement Learning for Keras.

koopman-rl

Data-driven Koopman control theory applied to reinforcement learning!

liif

Learning Continuous Image Representation with Local Implicit Image Function, in CVPR 2021 (Oral)

madpl

Task-oriented Dialog Policy Learning with Multi-Agent Reinforcement Learning

nlp_multi_task_learning_pytorch

A multitask learning architecture for Natural Language Processing of Pytorch implementation

pykrige

Kriging Toolkit for Python

pymarl

Python Multi-Agent Reinforcement Learning framework

pytorch-drl

PyTorch implementations of various Deep Reinforcement Learning (DRL) algorithms for both single agent and multi-agent.

rl-mpc-lanemerging

Combining Reinforcement Learning with Model Predictive Control for On-Ramp Merging

scale_net

Model for SCALE-Net: Scalable Vehicle Trajectory Prediction Network under Random Number of Interacting Vehicles via Edge-enhanced Graph Convolutional Neural Network

spektral

Graph Neural Networks with Keras and Tensorflow 2.

stellargraph

StellarGraph - Machine Learning on Graphs

stgcn_ijcai-18

Spatio-Temporal Graph Convolutional Networks

stockpredictionai

In this noteboook I will create a complete process for predicting stock price movements. Follow along and we will achieve some pretty good results. For that purpose we will use a Generative Adversarial Network (GAN) with LSTM, a type of Recurrent Neural Network, as generator, and a Convolutional Neural Network, CNN, as a discriminator. We use LSTM for the obvious reason that we are trying to predict time series data. Why we use GAN and specifically CNN as a discriminator? That is a good question: there are special sections on that later.

t-gcn

Temporal Graph Convolutional Network for Urban Traffic Flow Prediction Method

variational-recurrent-models

Codes for the study "Variational Recurrent Models for Solving Partially Observable Control Tasks", published as a conference paper at ICLR 2020 (https://openreview.net/forum?id=r1lL4a4tDB)