Using tabular and deep reinforcement learning methods to infer optimal market making strategies
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Jun 29, 2023 - Jupyter Notebook
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Using tabular and deep reinforcement learning methods to infer optimal market making strategies
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
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Apply Double Deep Q Learning
A Tetris AI using convolutional neuronal networks.
Deep RL for unsupervised hyperspectral band selection.
Play Super Mario Bros Game using Double Deep Q Network implemented in PyTorch.
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This project is a Double Deep Q learning Agent that learns to play the dice game Yahtzee
Deep reinforcement learning agent
Pytorch implementation of Double Deep Q Network (DDQN) learning with vectorized environments
Algorithmic Trading the Tesla Stock with Deep Reinforcement Learning. Paper:
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
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Double deep q network implementation in OpenAI Gym's "Mountain Car" environment
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