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

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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.

Apply Double Deep Q Learning

Deep Q Network and Double DQN implementation for OpenAI gym CartPole

Deep RL for unsupervised hyperspectral band selection.

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

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