Implementation of SARSA Semi-Gradient Method on the Mountain Car Open AI Environment.
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Updated
Dec 8, 2022 - Python
Implementation of SARSA Semi-Gradient Method on the Mountain Car Open AI Environment.
Own researches in reinforcement learning using openai-gym.
Deep Reinforcement learning applied on open AI MountainCar environment
An implementation of the paper "Reinforcement learning with a bilinear Q function" on the Mountain Car problem.
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.
Code for the Genetic Algorithms for Mapping Evolution (GAME), a project done at Johns Hopkins University during Fall 2022.
Reinforcement Learning Project - Mountain Car
Comparing VPG, TRPO and PPO from Policy Gradient family
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
Sutton's Mountain Car Problem with Value Iteration
University Course Assignment - Reinforcement Learning
This repository contains codes of deep deducing solving the classic control problems.
My programs during CS747 (Foundations of Intelligent and Learning Agents) Autumn 2021-22
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.
Double deep q network implementation in OpenAI Gym's "Mountain Car" environment
Implementing reinforcement learning algorithms using TensorFlow and Keras in OpenAI Gym
Deep RL toy example based on gym package with several methods
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