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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.
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.
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.
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.
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.
Application of reinforcement learning to the management of traffic light intersection
RL algorithm for stock trading with multiple reward functions
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.
Repo hosting the NMA Deep Learning - Lunar Lander transfer learning project repository for Group 1, Atcheke Pod (transferlanders)
Representation Abstractions as Incentives for Reinforcement Learning Agents: A Robotic Grasping Case Study