Tic-tac-toe/"noughts & crosses" written in Clojure (CLI + deps). AI powered by Monte Carlo tree search algorithm
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Updated
Aug 19, 2022 - Clojure
Tic-tac-toe/"noughts & crosses" written in Clojure (CLI + deps). AI powered by Monte Carlo tree search algorithm
Reinforcement Learning framework for learning IoT interactions.
This repository contains my implementations of well-known reinforcement learning algorithms for training a single agent. All implementations are done using PyTorch and Python >=3.6.
RLjs currently serves as an interactive playground for learning reinforcement learning.
Implementing different reinforcement learning algorithms on different gym environments and comparing results.
A collection of RL algorithms written in PyTorch
Implementation of RL algorithms to beat Atari 2600 games
Various RL algorithms implementations
Short own implementation of the game snake. In this project I'am using the ray library together with ray tune and a custom PPO model.
Custom Reinforcement Learning Agents
reinforcement learning DQN method to solve OpenAi Gym "LunarLander-v2" by usnig a Deep Neuralnetwork
Using reinforcement learning to play games.
Safe Reinforce Learning -> Constraint algorithms to train agents in Safety Gym, paper notes on research papers regarding RL with constraints + optimizer + neural networks, PyTorch implementation on policy gradient algorithms
This is the repository for Offline-Online Representation Learning for Reinforcement Learning.
Implémentations d'apprentissage par renforcement mixées avec des techniques de Deep Learning sur des environnements OpenAI Gym
This is a repository for Coursera Reinforcement Learning Course Notebook ,, these consist of my solutions. Feel Free to take a look , if you are stuck in Course and suggest corrections, if you find any mistake. Also Useful if you are looking for an implementation of RL-Algorithms. ** NOTE THESE NOTEBOOKS DON'T WORK AS THEY DO NOT CONTAIN UTILITY…
Navigation Project - Udacity Deep Reinforcement Learning Nanodegree
Code for some fun exercises in the textbook 'Reinforcement Learning - An Introduction'
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