OpenAI LunarLander-v2 DeepRL-based solutions (DQN, DuelingDQN, D3QN)

Implementation of reinforcement learning algorithms for the OpenAI Gym environment LunarLander-v2

Muesli RL algorithm implementation (PyTorch) (LunarLander-v2)

Implement RL algorithms in PyTorch and test on Gym environments.

Solving OpenAI Gym's Lunar Lander environment using Deep Reinforcement Learning

We apply DQN algorithm to make and artificial agent learn how to land space-craft on moon.

RL with OpenAI Gym

Teaching to an agent to play the Lunar Lander game from OpenAI Gym using REINFORCE.

PyTorch application of reinforcement learning algorithm in OpenAI LunarLander - DDPG

reinforcement learning Double Deep Q Learning (DDQN) method to solve OpenAi Gym "LunarLander-v2" by usnig Double Deep NeuralNetworks

LunarLander-v2 learning how to land efficiently using DQN and DDQN for training

Deep RL implementations. DQN, SAC, DDPG, TD3, PPO and VPG implemented in pytorch. Tested Env: LunarLander-v2 and Pendulum-v0.

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 🕹️

This is a Deep Reinforcement Learning solution for the Lunar Lander problem in OpenAI Gym using dueling network architecture and the double DQN algorithm.

Experiment 1: Comparison of key bandit algorithms; Experiment 2: Comparison of Q and SARSA Learning on Taxiv3 environment' ; Experiment 3: Comparison of Q, SARSA and CEM Learning on LunarLanderv2 Environment

This project uses the pytorch package to implement DQN and DDPG models to automate the LunarLander-v2 and LunarLanderContinuous-v2 games.

This repository includes all ML, DL and RL projects that I have done.

Implementation of reinforcement learning algorithms for the OpenAI Gym environment LunarLander-v2