#Deep-q-network
Showing 24 of 24 repositories tagged #deep-q-network, ranked by stars
Simple Reinforcement learning tutorials, 莫烦Python 中文AI教学
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
Minimal and Clean Reinforcement Learning Examples
Minimal Deep Q Learning (DQN & DDQN) implementations in Keras
Contains high quality implementations of Deep Reinforcement Learning algorithms written in PyTorch
Deep Reinforcement Learning based Trading Agent for Bitcoin
StarCraft II - pysc2 Deep Reinforcement Learning Examples
Code for paper "Computation Offloading Optimization for UAV-assisted Mobile Edge Computing: A Deep Deterministic Policy Gradient Approach"
A reinforcement learning package for Julia
CURL: Contrastive Unsupervised Representation Learning for Sample-Efficient Reinforcement Learning
Trained A Convolutional Neural Network To Play 2048 using Deep-Reinforcement Learning
Forex trading simulator environment for OpenAI Gym, observations contain the order status, performance and timeseries loaded from a CSV file containing rates and indicators. Work In Progress
A collection of various RL algorithms like policy gradients, DQN and PPO. The goal of this repo will be to make it a go-to resource for learning about RL. How to visualize, debug and solve RL problems. I've additionally included playground.py for learning more about OpenAI gym, etc.
Deep Reinforcement Learning For Trading
Reinforcement learning (RL) implementation of imperfect information game Mahjong using markov decision processes to predict future game states
Train a DQN Agent to play CarRacing 2d using TensorFlow and Keras.
Deep Convolutional Q learning based Self learning implementation for Subway Surfers game
💥💥 This is a easy installable extension for OpenAi Gym Environment. This simulates SpaceX Falcon landing.
Tetris with a Deep Q Network.
[INACTIVE] A collection of various machine learning solver. The library is an object-oriented approach (baked with Typescript) and tries to deliver simplified interfaces that make using the algorithms pretty simple.
Emergent Trading Strategies with DQN in Stock Market Trading This repository contains the implementation of a Deep Q-Network (DQN), applied to the realm of stock market trading. This repository also holds the code for research paper "Emergent Trading Strategies from Deep Reinforcement Learning Models in Stock Market Trading".