Code for the Genetic Algorithms for Mapping Evolution (GAME), a project done at Johns Hopkins University during Fall 2022.

Own researches in reinforcement learning using openai-gym.

An implementation of the paper "Reinforcement learning with a bilinear Q function" on the Mountain Car problem.

Deep Reinforcement learning applied on open AI MountainCar environment

Reinforcement learning algorithm implementation for "Artificial Intelligence" course project, La Sapienza, Rome, Italy, 2018

Reinforcement Learning Project - Mountain Car

Sutton's Mountain Car Problem with Value Iteration

University Course Assignment - Reinforcement Learning

solve let do irl

Implementation of SARSA Semi-Gradient Method on the Mountain Car Open AI Environment.

Python implementation of the Particle Swarm Optimization algorithm and some variants

Deep RL toy example based on gym package with several methods

This repo implements Deep Q-Network (DQN) for solving the Mountain Car v0 environment (discrete version) of the Gymnasium library using Python 3.8 and PyTorch 2.0.1 with a custom reward function for faster convergence.

Q Learning, SARSA, Expected SARSA to solve OpenAI's gym.mountain_car environment

Implementing reinforcement learning algorithms using TensorFlow and Keras in OpenAI Gym

This repo is for playing with reinforcement learning algorithms. I am either using openai gym or ViZDoom as an environment.

APReL: Active preference-based reward learning for human-robot interaction. Utilizing "Mountain Car" environment, learn from human preferences to reach the goal state. Applications in robotics and adaptability to other learning methods.

Mountain Car is a Reinforcement Learning task that aims to learn the policy of climbing a steep hill and reaching the flag-marked goal. we use Q-learning to find the optimal policy in each case.

Double deep q network implementation in OpenAI Gym's "Mountain Car" environment