We investigate the (deep) Q-learning algorithm on different environments and measure the performance of our agents.
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Updated
Jun 2, 2024 - Jupyter Notebook
We investigate the (deep) Q-learning algorithm on different environments and measure the performance of our agents.
C++-based high-performance parallel environment execution engine (vectorized env) for general RL environments.
Reinforcement Learning environments based on the 1993 game Doom
A modular Deep Reinforcement Learning library that supports multiple environments, made with Python 3.6.
Reinforcement Learning on ViZDoom Enviroment using Advantage Actor Critic and Dueling Deep Q Networks.
An AI which trains to win more and more indecently the Doom Deadly Corridor (ViZDoom)
Deep reinforcement learning agents that play Doom using Python.
This project implements an agent for playing the VizDoom game on various levels using the Proximal Policy Optimization (PPO) algorithm from the stablebaselines3 library. The agent is trained to learn the optimal actions to take at each step in the game in order to complete the level and maximize the score.
Implemented DQN with Intrinsic Curiosity Module for a VizDoom competition at nate.
vizDoom AI is a study project realized by me at the 3WA, to realize this model of artificial intelligence, I was greatly inspired by the videos of Nicholas Renotte (Youtubeur that I appreciate particularly). The goal of this script is to learn how to play all levels of Doom by himself and be the best he can be.
Applying representation learning to reinforcement learning
Solving games with reinforcement learning
Reinforcement learning models in ViZDoom environment
Bot that learns through reinforcement learning (RL) how to play DOOM🤖
OpenAI Gym wrapper for ViZDoom enviroments
Using Tensorflow 2.2 & 2.4 to train my bots to fight for my entertainment.
Implementation of the DQN and DRQN algorithms in Keras and tensorflow
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