Challenging dexterous manipulation environments for RL that extend the hand manipulation environments introduced in OpenAI's Gym

Sim-to-real RL for in-hand cube rotation with the LEAP Hand, built on Mjlab.

Simulation of a robotic hand in MuJoCo, grasping objects using Reinforcement Learning (RL)

Repository for our paper "Robust In-Hand Reorientation with Hierarchical RL-Based Motion Primitives and Model-Based Regrasping" submitted to IEEE RA-P

No description available

A structured, hands-on learning journey into Reinforcement Learning (RL). Whether you're a beginner or experienced in AI, this repository helps you master everything from Q-learning to Multi-Agent RL, Meta RL, and Safe RL.

This project provides a hands-on tutorial for understanding and implementing the Proximal Policy Optimization (PPO) algorithm to fine-tune Large Language Models (LLMs) using Reinforcement Learning (RL). It is inspired by the logic found in the TinyZero repository but significantly simplified for pedagogical purposes.

We will get introduced to reinforcement learning and also implement a simple example of the same in Python. It will be a basic code to demonstrate the working of an RL algorithm. Brief exposure to object-oriented programming in Python, machine learning, or deep learning will also be a plus point.

Practicing RL Algorithms — A hands-on reinforcement learning project where you implement algorithms by filling in guided templates, training on LunarLander-v3 with shared infrastructure for training, evaluation, and visualization.

Using Reinforcement Learning to teach Open AI hand robot to immitate the exact positions as seen in the simulated MuJoCoPy environment. RL algorithm of PPO is extensivelly used to achieve the goal.

This repository serves as a hands-on study of Reinforcement Learning algorithms. It began with ground-up implementations of well-known RL algorithms to gain a deeper understanding of their inner workings and culminated in testing the Stable Baselines3 framework.

Welcome to our AI Exploration Hub! This repository houses an extensive collection of diverse AI projects, spanning ML, DL, NLP, CV, and RL, offering hands-on experiences, tutorials, pre-trained models, and cutting-edge research to foster innovation in the world of artificial intelligence.

RL based learning for a robotic arm to imitate a given hand in a image feed

This repository provides a hands-on Reinforcement Learning (RL) tutorial that starts from core RL concepts and gradually builds up to an advanced autonomous robot navigation system in simulation using Deep Reinforcement Learning.

Experienced 4+ Yrs across the full AI/ML lifecycle, from DE , DS and model development to API driven deployment, cloud infrastructure, and monitoring. Strong hands on expertise in ML (LR, SVM, Random Forest, XGBoost, ARIMA, SARIMA, Time Series, Forecasting, RL), DL(CNN, RNN, GANs, Yolo8 ,Transformers),Computer Vision, NLP, GenAI & Agentic AI.

Reinforcement learning algorithms implementations with hrl.boyuai and Berkeley cs285.

Jupyter Notebook accompanying a blog post around Reinforcement Learning

Exploring emergent trading behaviour in a multi-agent stochastic market.

No description available

Express.js route handler missing error handling for invalid user ID. The code attempts to parse the ID as an integer without checking if it's a valid number, leading to potential crashes or unexpected behavior.

R code throws an error if the specified CSV file is not found.

No description available

The R function `calculate_mean` does not correctly handle NA (Not Available) values when calculating the mean of a numeric vector. It should remove NA values before calculating the mean to avoid errors.

Swift function to calculate the area of a rectangle that does not handle invalid inputs such as negative width or height values. This can lead to unexpected results or crashes. The solution adds input validation to handle such cases.

Lua's handling of nil in function arguments can lead to unexpected behavior, especially when combined with conditional logic. This example demonstrates how omitting checks for specific nil values can cause unexpected results.

This code demonstrates a common error in Python: handling empty lists and type errors. The function `calculate_average` correctly handles empty lists, but it doesn't account for cases where the input list contains non-numeric values, leading to a `TypeError`.

Hands-on: Tutorial of RL Tabular Method in the Grid world

Hands-on Reinforcement Learning implementation in Python . Includes basic RL, policy gradients, deep Q-learning, and advanced RL algorithms.

This repository contains my personal notes and hands-on implementations for fine-tuning and post-training Large Language Models (LLMs).

Express.js app crashes without proper error handling for database errors or missing users. Improper error handling leads to unexpected behavior or crashes.