Trading Bot using Double Deep Reinfocement Learning

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

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

A Tetris AI using convolutional neuronal networks.

Using Double Deep Q-Network to learn to play Minesweeper game

Reinforcement learning agent for OpenAI's Car Racing environment

Deep reinforcement learning agent

Deep RL for unsupervised hyperspectral band selection.

Apply Double Deep Q Learning

Deep Q Network and Double DQN implementation for OpenAI gym CartPole

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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 tabular and deep reinforcement learning methods to infer optimal market making strategies