Learning through Exploration- Reinforcement

Project Title

Learning through Exploration- Reinforcement

Project Area of Specialization Artificial IntelligenceProject Summary

The goal is to use reinforcement learning where certain
optimal sequential decision (action) is made using evaluative
feedback without supervised labels. In other terms, consider a reinforcement learning problem implemented on a gaming
environment. A set of rules for the game will need to be specified; a reinforcement learning AI will then play a simulated version of the game following the rules hundreds or thousands of times, each time attempting to learn a better strategy through trial and error. The exact strategy for playing the game cannot be written in code and will be devised by the machine through exploration and reinforcement.

Project Objectives

making a gamebot who can play a game for which it will be trained so that it can play more like human and less like a machine so  that if anyone who plays with it should feel like playing with a human player.

Project Implementation Method

this project will be huge help in gaming industry when pro players want to test their abilities and practice before big games.

Benefits of the Project

if anyone wants to play a game he does not have a person with him to play with he can play with this gamebot and challlenge himsellf and can enjoy.

Technical Details of Final Deliverable

we will work on some algorthims i.e monte carlo and alpha beta pruning so that our projects works well and fine

Final Deliverable of the Project Software SystemCore Industry OthersOther IndustriesCore Technology Artificial Intelligence(AI)Other TechnologiesSustainable Development Goals Partnerships to achieve the GoalRequired Resources