KTH Artificial Intelligence (DD2380) final project VRP implemented in IP, PDDL and RL

"Adaptive Cruise Control for a Hybrid Vehicle with Deep Policy Gradients". Final project for ECE 517/414 Reinforcement Learning.

Final project for the AIT-3007 course

This repository will be used to share all documents related to CS282 RL in Healthcare Final Project.

RL final project

ECE 276C Final Project: Mobile manipulation around obstacles using MPC and Vision-Based RL

RL final project

Based on the following MATLAB examples: "Train DQN Agent with LSTM Network to Control House Heating System" and "House Heating System". Additional guidance taken from the paper “Demonstration of Intelligent HVAC Load Management With Deep Reinforcement Learning: Real-World Experience of Machine Learning in Demand Control" by Du, et. al. 2022.

Final Project #2 for Honors Computer Science. New RL Clock by Marco Suri, Dylan Pan, Austin Reid, Avish Kumar, and Michael DiLallo

RBE 595 RL Project - Quadrotor on Airsim with RL and Vision

DDPG+HER with demonstration RL grasping experiments for CS6731 final project

No description available

Repository for RL final project

An empirical study of goal-conditioned RL

No description available

NTU-RL-Course Final Project 2024

This is the final project for Reinforcement Learning (RL)

This project uses Reinforcement Learning to teach an agent to drive by itself and learn from its observations so that it can maximize the reward(180+ lines)

+ These codes reproduces a project rejected by ICML using RL to learn a pivot in simplex method, it's a final project for the Algorithms in Large Data in PKU

Our project focuses on the problem of generating synthetic levels of a game such that the levels can be used to learn an optimal policy for playing the game. Given a few pre-existing game levels we want to use deep generative models (like GANs) to generate new additional game levels. We will then train an RL agent on these levels to learn a generalized policy of playing the game. Our hypothesis is that training the agent with the additional levels will lead to an optimal policy that performs better than the policy learned from the few pre-existing levels. Our final objective is to learn such a generative model, train an RL agent with the generated levels and test its performance on a set of unseen game levels.

RL Final Project :

Final Year Project on Inventory management system using RL

KAIST CS672 RL Final Project

No description available

No description available

Vision-based Control for Fixed-wing UAVs

No description available

Solve maze problem using Deep Q Learning

Final Project for Reinforcement Learning Course

A custom reinforcement learning environment in Webots features an autonomous vehicle on a fixed track with randomized obstacles. SAC and PPO are used for training, with Uniform Domain Randomization to enhance generalization. Results show improved stability, efficiency, and robustness, supporting sim-to-real transfer.