This project is a Double Dee 8000 p Q learning Agent that learns to play the dice game Yahtzee
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Updated
Mar 12, 2025 - Python
8000
This project is a Double Dee 8000 p Q learning Agent that learns to play the dice game Yahtzee
Model-free, off-policy reinforcement learning with DQN's on Gym's environments
This project uses Deep Q-Learning to train a Mario agent in a reinforcement learning environment. The agent is optimized using dynamic exploration rates, custom reward shaping, and Prioritized Experience Replay to improve learning efficiency.
Clean, modular DQN in PyTorch with Double/Dueling options and MLP/CNN/LSTM backbones—plug-and-play for Gymnasium environments.
Learning Mario Agent with the Double Deep Q-Learning Algorithm in the Gym Super Mario Environment.
DQN stock-trading agent with a custom Gymnasium environment and yfinance data.
Pytorch implementation of Double Deep Q Network (DDQN) learning with vectorized environments
Play Super Mario Bros Game using Double Deep Q Network implemented in PyTorch.
Double deep q network implementation in OpenAI Gym's "Mountain Car" environment
Reinforcement learning implementation on C++
Undergraduate Dissertation (University of Malta) 2020-2023 - 'Autonomous Drone Control using Reinforcement Learning''
Implementation of the Double Deep Q-Learning algorithm with a prioritized experience replay memory to train an agent to play the minichess variante Gardner Chess
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.
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