Training of Drone Swarms using StableBaselines3, PettingZoo, AirSim and UE4

A hybrid collision avoidance system combining Deep Reinforcement Learning with Model Predictive Control, designed for autonomous vehicles in CARLA to navigate scenarios with stationary obstacles.

An end-to-end (E2E) reinforcement learning model for autonomous vehicle collision avoidance in the CARLA simulator, using a recurrent PPO algorithm for dynamic control. The model processes RGB camera inputs to make real-time acceleration and steering decisions.

The project presents a drone obstacle avoidance system using Microsoft AirSim and the DDPG algorithm, training drones with LIDAR and depth sensors for improved real-time navigation. It compares the implementation of DDPG algorithm with different sensors and their combination.

Reinforcement learning for crawling robot

RL algorithm for stock trading with multiple reward functions

Repository with all source files relating to the 6CCE3EEP Final Year Project titled "Self Parking with Reinforcement Learning." The project was implemented using Python, and used PyGame, OpenAI Gym, and the Stable Baselines-3 libraries in order to implement a Proximal Policy Optimisation (PPO) algorithm.

Reinforcement Learning Testbed for Quantitative Trading

Training of Drone Swarms using StableBaselines3, PettingZoo, AirSim and UE4

Application of reinforcement learning to the management of traffic light intersection

This repository contains the implementation of a wide variety of Reinforcement Learning Projects in different applications of Bandit Algorithms, MDPs, Distributed RL and Deep RL. These projects include university projects and projects implemented due to interest in Reinforcement Learning.

State Representations as Incentives for Reinforcement Learning Agents: A Sim2Real Analysis on Robotic Grasping

Water resources exploring and reservoir operations Using Multi-Agent Deep Reinforcement Learning.

This repo contains our project for CPE 800 which focuses on reinforcement learning for stock prediction

This repository hosts the code and resources for a comprehensive study on optimizing greenhouse conditions using Reinforcement Learning algorithms such as PPO, A2C, and SAC. For detailed results, explanation of the environments, and the algorithms, please refer to the accompanying report.

Reinforce learning gym for Elden Ring, based on gymnaium and stable baseline3, PPO

PPO agent and A2C agents for Flappybird. Includes scripts, training code, and evaluation tools.

This repository contains an implementation of stable bipedal locomotion control for humanoid robots using the Soft Actor-Critic (SAC) algorithm, simulated within the MuJoCo physics engine and trained using Gymnasium and Stable Baselines 3.

Autonomous Navigation with Deep Reinforcement Learning in CARLA

This repository hosts Jupyter notebooks showcasing the training of Atari games using a variety of Deep Reinforcement Learning (RL) algorithms such as Proximal Policy Optimization (PPO), Deep Deterministic Policy Gradient (DDPG), Deep Q-Networks (DQN), Advantage Actor-Critic (A2C), and more.