Tabular methods for reinforcement learning
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Updated
Jul 3, 2020 - Python
Tabular methods for reinforcement learning
path planning using Q learning algorithm
Demonstration of Q-Learning and SARSA algorithms utilizing Python and OpenAI GYM
This github contains a simple OpenAi Gym Maze Enviroment and (at now) a RL Algorithm to solve it.
Reinforcement learning algorithm implements.
Solutions for OpenAI Gym RL environments
Applying PBT optimization technique to different domains
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.
Using the SARSA to beat the environment, Windy Gridworld. Implement in C++.
The implementation of some reinforcement learning techniques like (Q-learning, SARSA, DQN) in two assignments and one big project.
Implementation of SARSA algorithm for path planning
Pac-Man RL Agent
Reinforcement learning system using the SARSA-RL Algorithm to learn to play a simple physics game, referred to as the The Acrobat Game
Temporal Difference methods - A simple implementation of SARSA algorithm applied to OpenAI gym's "CliffWalking" environment.
人工智能课程的实验
PacmanRL - Reinforcement Learning for Pacman (Q-Learning / SARSA)
Two reinforcement learning algorithms (Standard SARSA Control and Tabular Dyna-Q) where an agent learns to traverse a randomly generated maze
University of Tehran-Reinforcement Learning Fall 2022
OpenAI_gym_Taxi-v2 solved with reinforcement learning - Expected Sarsa
Various Reinforcement Learning Algorithms on Racetrack Simulations
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