Popular GitHub Repositories for Tennis

Machine Learning Model for Sport Predictions (Football, Basketball, Baseball, Hockey, Soccer & Tennis)

Apply computer vision to table tennis for match / training analysis

Tennis Environment - Multi-Agent Reinforcement Learning

Proximal Policy Optimization with Beta distribution - uses multi agent Unity ML Tennis

? Multi-Agent Proximal Policy Optimization approach to a competitive reinforcement learning problem

Tennis match analysis via computer vision techniques.

This is Multi agent deep reinforcement learning repo which trains an agent to play Tennis. It trains by playing against itself.

Multi-Agent training using Deep Deterministic Policy Gradient Networks, Solving the Tennis Environment

Tennis Game play using Multi Agent DDPG - Deep Reinforcement Learning

In this repo, I implement deep deterministic policy gradients and multi-agent deep deterministic poilicy gradients to solve the Tennis enironment (Unity ML-Agents)

Multi-agent reinforcement learning framework for Unity environments. Implements MAPPO, MASAC, MATD3, and MADDPG with comprehensive evaluation tools. Features sample-efficient training, competitive analysis, and pre-trained models achieving great performance in Tennis and Soccer environments.

A project created with ML-Agents for AIs that play table tennis.

This Repository Contains PyTorch Implementation of Multi Agent DDPG Algorithm to train agents to play tennis.

An interactive Jupyter Notebook that leverages Retrieval-Augmented Generation (RAG) to build a “knowledge worker” for tennis. Ask natural-language questions about players, matches, and stats, and get precise, sourced answers powered by OpenAI’s API and a custom vector store.

MADDPG Implementation on Pytorch

Multi-Agent-RL Competition on Unitys Tennis Environment

An implementation of MADDPG multi-agent to solve a Unity environment like Tennis and Soccer.

Multi-agent reinforcement learning where an agent learns to play tennis against itself. Part of the coursework for Udacity's Deep RL Nanodegree.

A predictive model for the association of tennis professional players with the use of various features such as player statistics, past performance, and physical attributes to predict the likelihood of a player's success in future matches and tournaments. This machine learning used the linear regression analysis to make its predictions.

Training a pair of agents to play tennis