Own researches in reinforcement learning using openai-gym.
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Updated
Jul 27, 2021 - Python
Own researches in reinforcement learning using openai-gym.
Implementation of SARSA Semi-Gradient Method on the Mountain Car Open AI Environment.
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
Implementing reinforcement learning algorithms using TensorFlow and Keras in OpenAI Gym
Deep RL toy example based on gym package with several methods
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.
Sutton's Mountain Car Problem with Value Iteration
University Course Assignment - Reinforcement Learning
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.
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