pikapika's repositories
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).
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)