RewardShifting

Tensorflow tutorial from basic to hard, 莫烦Python 中文AI教学

Implementations from the free course Deep Reinforcement Learning with Tensorflow and PyTorch

Fast and flexible AutoML with learning guarantees.

Minimal Deep Q Learning (DQN & DDQN) implementations in Keras

A unified ensemble framework for PyTorch to improve the performance and robustness of your deep learning model.

We present MocapNET, a real-time method that estimates the 3D human pose directly in the popular Bio Vision Hierarchy (BVH) format, given estimations of the 2D body joints originating from monocular color images. Our contributions include: (a) A novel and compact 2D pose NSRM representation. (b) A human body orientation classifier and an ensemble of orientation-tuned neural networks that regress the 3D human pose by also allowing for the decomposition of the body to an upper and lower kinematic hierarchy. This permits the recovery of the human pose even in the case of significant occlusions. (c) An efficient Inverse Kinematics solver that refines the neural-network-based solution providing 3D human pose estimations that are consistent with the limb sizes of a target person (if known). All the above yield a 33% accuracy improvement on the

Deep Reinforcement Learning based Trading Agent for Bitcoin

StarCraft II - pysc2 Deep Reinforcement Learning Examples

A reinforcement learning package for Julia

NLP in Python with Deep Learning