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Implementation of Reinforcement Learning Algorithms. Python, OpenAI Gym, Tensorflow. Exercises and Solutions to accompany Sutton's Book and David Silver's course.
Gathers machine learning and deep learning models for Stock forecasting including trading bots and simulations
Tutorials on implementing a few sequence-to-sequence (seq2seq) models with PyTorch and TorchText.
Repo for the Deep Reinforcement Learning Nanodegree program
Fault-tolerant, highly scalable GPU orchestration, and a machine learning framework designed for training models with billions to trillions of parameters
Pytorch Implementation of DQN / DDQN / Prioritized replay/ noisy networks/ distributional values/ Rainbow/ hierarchical RL
The repository for freeCodeCamp's YouTube course, Algorithmic Trading in Python
Solutions of Reinforcement Learning, An Introduction
Quantitative analysis, strategies and backtests
Contains high quality implementations of Deep Reinforcement Learning algorithms written in PyTorch
Environment for reinforcement-learning algorithmic trading models
An libary to price financial options written in Python. Includes: Black Scholes, Black 76, Implied Volatility, American, European, Asian, Spread Options
Reinforcement Learning examples implementation and explanation
Implementations of algorithms from the Q-learning family. Implementations inlcude: DQN, DDQN, Dueling DQN, PER+DQN, Noisy DQN, C51
Yet another black-box optimization library for Python
DQN-Atari-Agents: Modularized & Parallel PyTorch implementation of several DQN Agents, i.a. DDQN, Dueling DQN, Noisy DQN, C51, Rainbow, and DRQN
Overview of State-of-the-art Deep Learning Based Methods for Time Series Forecasting
A Python implementation of the Longstaff-Schwartz linear regression algorithm for the evaluation of call rights an American options.
Efficient Exploration through Bayesian Deep Q-Networks
Source for the sample efficient tabular RL submission to the 2019 NIPS workshop on Biological and Artificial RL