Using tabular and deep reinforcement learning methods to infer optimal market making strategies
-
Updated
Jun 29, 2023 - Jupyter Notebook
Using tabular and deep reinforcement learning methods to infer optimal market making strategies
Reinforcement learning agent for OpenAI's Car Racing environment
Deep reinforcement learning agent
Deep Q Network and Double DQN implementation for OpenAI gym CartPole
Trading Bot using Double Deep Reinfocement Learning
Apply Double Deep Q Learning
Project1 Navigation
Deep RL for unsupervised hyperspectral band selection.
A Tetris AI using convolutional neuronal networks.
Play Super Mario Bros Game using Double Deep Q Network implemented in PyTorch.
Pytorch implementation of Double Deep Q Network (DDQN) learning with vectorized environments
This project trains an agent to navigate and to collect bananas in a continuous square environment. The environment is based on the Unity Machine Learning Agents Toolkit
Environment-related difference of Deep Q-Learning and Deep Double Q-Learning
The following project concerns the development of an intelligent agent for the famous game produced by Nintendo Super Mario Bros. More in detail: the goal of this project was to design, implement and train an agent with the Q-learning reinforcement learning algorithm.
Using Double Deep Q-Network to learn to play Minesweeper game
This project is a Double Deep Q learning Agent that learns to play the dice game Yahtzee
Double deep q network implementation in OpenAI Gym's "Mountain Car" environment
Add a description, image, and links to the double-deep-q-network topic page so that developers can more easily learn about it.
To associate your repository with the double-deep-q-network topic, visit your repo's landing page and select "manage topics."