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.
Deep Q Network and Double DQN implementation for OpenAI gym CartPole
A Tetris AI using convolutional neuronal networks.
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 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.
Environment-related difference of Deep Q-Learning and Deep Double Q-Learning
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
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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