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 t
Learning through Exploration- Reinforcement
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.
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.
this project will be huge help in gaming industry when pro players want to test their abilities and practice before big games.
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.
we will work on some algorthims i.e monte carlo and alpha beta pruning so that our projects works well and fine
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