There are 2 repositories under mountain-car topic.
Python code, PDFs and resources for the series of posts on Reinforcement Learning which I published on my personal blog
Solving OpenAI Gym problems.
Solutions and figures for problems from Reinforcement Learning: An Introduction Sutton&Barto
In this all Projects dealing with reinforcement learning wil be uploaded
A simple baseline for mountain-car @ gym
Implementation of Reinforcement Algorithms from scratch
solution to mountain car problem of OpenAI Gym
:space_invader: My solutions to OpenAI Gym Reinforcement Learning problems.
Reinforcement Learning algorithms SARSA, Q-Learning, DQN, for Classical and MuJoCo Environments and testing them with OpenAI Gym.
Reinforcement learning algorithms to solve OpenAI gym environments
A car is on a one-dimensional track, positioned between two "mountains". The goal is to drive up the mountain on the right; however, the car's engine is not strong enough to scale the mountain in a single pass. Therefore, the only way to succeed is to drive back and forth to build up momentum.
Implementing reinforcement learning algorithms using TensorFlow and Keras in OpenAI Gym
Deep RL toy example based on gym package with several methods
Comparing VPG, TRPO and PPO from Policy Gradient family
Code for the Genetic Algorithms for Mapping Evolution (GAME), a project done at Johns Hopkins University during Fall 2022.
Inverse Reinforcement Learning Algorithm implementation with Python
This repo is for playing with reinforcement learning algorithms. I am either using openai gym or ViZDoom as an environment.
Q Learning, SARSA, Expected SARSA to solve OpenAI's gym.mountain_car environment
Reinforcement learning algorithm implementation for "Artificial Intelligence" course project, La Sapienza, Rome, Italy, 2018
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.
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.
My programs during CS747 (Foundations of Intelligent and Learning Agents) Autumn 2021-22
This repository contains codes of deep deducing solving the classic control problems.
An implementation of the paper "Reinforcement learning with a bilinear Q function" on the Mountain Car problem.
Double deep q network implementation in OpenAI Gym's "Mountain Car" environment
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.
Sutton's Mountain Car Problem with Value Iteration
University Course Assignment - Reinforcement Learning
Own researches in reinforcement learning using openai-gym.
Reinforcement Learning Project - Mountain Car