Deep RL based solution of LunarLander-v2 environment.
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Updated
Apr 4, 2019 - Jupyter Notebook
Deep RL based solution of LunarLander-v2 environment.
This is a project of reinforcement learning which contains two different environments. The first environment is the taxi driver problem in 4x4 space with the simple Q-learning update rule. In this task, we compared the performance of the e-greedy policy and Boltzmann policy. As a second environment, we chose the LunarLander from the open gym. Fo…
Behaviour Cloning On OpenAI Environment
PPO Clip first-order method for the LunarLander discrete environment
Research internship during the 5th semester of my B.Sc CBT degree @ TUM CS
Trained model of a PPO agent playing LunarLander-v2 using the stable-baselines3 library.
Self-solving control problems from OpenAI Gym with NEAT
Deep Reinforcement Learning on Lunar Lander gym environment
This repository contains a re-implementation of the Proximal Policy Optimization (PPO) algorithm, originally sourced from Stable-Baselines3.
Useless try to create neural network
Deep Q-Network aplicado no OpenAI Gym's LunarLander-v2 environment
Deep Q-Network example from Udacity's Deep Reinforcement Learning Nanodegree.
Deep learning and Neural Networks course labs&homeworks&assignments
This is a Deep Reinforcement Learning solution for the Lunar Lander problem in OpenAI Gym using dueling network architecture and the double DQN algorithm.
Deep Q-Learning algorithms to solve LunarLander-v2.
Implementation of reinforcement learning algorithms for the OpenAI Gym environment LunarLander-v2
Gradient Free Reinforcement Learning solving Openai gym LunarLanderV2 by Evolution Strategy (Genetic Algorithm)
Semester project for the AI Applications class of the MSc in Artificial Intelligence
📖 Paper: Continuous control with deep reinforcement learning 🕹️
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