Research internship during the 5th semester of my B.Sc CBT degree @ TUM CS
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Updated
Aug 18, 2024 - Python
Research internship during the 5th semester of my B.Sc CBT degree @ TUM CS
PPO Clip first-order method for the LunarLander discrete environment
Trained model of a PPO agent playing LunarLander-v2 using the stable-baselines3 library.
📖 Paper: Continuous control with deep reinforcement learning 🕹️
Muesli RL algorithm implementation (PyTorch) (LunarLander-v2)
reinforcement learning Double Deep Q Learning (DDQN) method to solve OpenAi Gym "LunarLander-v2" by usnig Double Deep NeuralNetworks
This is a Deep Reinforcement Learning solution for the Lunar Lander problem in OpenAI Gym using dueling network architecture and the double DQN algorithm.
This repository contains a re-implementation of the Proximal Policy Optimization (PPO) algorithm, originally sourced from Stable-Baselines3.
Gradient Free Reinforcement Learning solving Openai gym LunarLanderV2 by Evolution Strategy (Genetic Algorithm)
Solving OpenAI Gym's Lunar Lander environment using Deep Reinforcement Learning
LunarLander-v2 learning how to land efficiently using DQN and DDQN for training
Deep Q-Network aplicado no OpenAI Gym's LunarLander-v2 environment
PyTorch application of reinforcement learning algorithm in OpenAI LunarLander - DDPG
This project uses the pytorch package to implement DQN and DDPG models to automate the LunarLander-v2 and LunarLanderContinuous-v2 games.
Implement RL algorithms in PyTorch and test on Gym environments.
Implementation of reinforcement learning algorithms for the OpenAI Gym environment LunarLander-v2
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
Semester project for the AI Applications class of the MSc in Artificial Intelligence
OpenAI LunarLander-v2 DeepRL-based solutions (DQN, DuelingDQN, D3QN)
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