Using tabular and deep reinforcement learning methods to infer optimal market making strategies
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Updated
Jun 29, 2023 - Jupyter Notebook
Using tabular and deep reinforcement learning methods to infer optimal market making strategies
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Apply Double Deep Q Learning
Deep RL for unsupervised hyperspectral band selection.
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.
Deep Q Network and Double DQN implementation for OpenAI gym CartPole
A Tetris AI using convolutional neuronal networks.
Deep reinforcement learning agent
Using Double Deep Q-Network to learn to play Minesweeper game
Reinforcement learning agent for OpenAI's Car Racing environment
Trading Bot using Double Deep Reinfocement Learning
Play Super Mario Bros Game using Double Deep Q Network implemented in PyTorch.
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
Double deep q network implementation in OpenAI Gym's "Mountain Car" environment
This project is a Double Deep Q learning Agent that learns to play the dice game Yahtzee
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