Reinforcement learning tutorials

This repository hosts a customized PPO based agent for Carla. The goal of this project is to make it easier to interact with and experiment in Carla with reinforcement learning based agents -- this, by wrapping Carla in a gym like environment that can handle custom reward functions, custom debug output, etc.

Adversarial attacks on Deep Reinforcement Learning (RL)

Deep Reinforcement Learning for Trading

💡 Grasp - Pick-and-place with a robotic hand 👨🏻‍💻

Multi agent gym environment based on the classic Snake game with implementations of various reinforcement learning algorithms in pytorch

Developed a highly customizable OpenAI gym environment and trained a stable_baselines3 PPO agent. Used the expert agent for Imitation Learning with DAgger

Snake game environment integrated with OpenAI Gym. Proximal Policy Optimization (PPO) implementation for training. Visualization of training progress and agent performance. Easy to understand code.

The aim of this repository is the analysis and study of computer intelligence and in-depth learning techniques in the development of intelligent gaming agents.

In this project, I have tried to use DeepRL for optimizing the selection of transactions done by the miner to increase the fee when they execute a block on the chain

An adaptive Machine Reinforcement Learning (MRL) system is being developed to gather and analyze media data using web scraping, training models to predict outcomes in areas like stock market trends, sports events, and other performance domains. It continuously refines its strategies based on real-time data and evolving patterns.

Modular Reinforcement Learning in PyTorch.

AI agent learns to walk, run, hop and crawl with out any given data using proximal policy optimisation.

Repository for the final project of the "Computational Intelligence" course @ PoliTo, 2022/2023

Personal project - attempting to train an RL model to trade crypto/other markets

Building an LLM with RLHF involves fine-tuning using human-labeled preferences. Based on Learning to Summarize from Human Feedback, it uses supervised learning, reward modeling, and PPO to improve response quality and alignment.

This repository contains the code for a project paper for a Master's module in the field of reinforcement learning. The aim of the project is to explore and implement Proximal Policy Optimization (PPO) agents to learn and play the 7x7 Hex game.

Injecting object-level implicit priors in a PPO agent with contrastive learning algorithm ST-DIM.

Automated gaming using machine learning

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