RPS Game with simple machine learning Human players of the Rock Paper Scissors game try to develop strategies to beat their opponents. Therefore, humans generally do not make random choices. Instead, their choices exhibit patterns that a computer can discover and exploit using a simple machine learning algorithm The computer's ML choice algorithm Continuously record the last N choices between the human and the computer player. Throw out the oldest choice in order to add a new one. For example, suppose N = 5 and during the game, the recorded choices are (the human's choices are underlined): PSRSP The last choice was made by the human, and it was paper. For each recorded sequence that ends with the human's choice, the computer should store how many times that sequence has occurred (each sequence's frequency). For example, for N 5, some of the stored sequences and their frequencies may be (in no particular order, the human choices are underlined) Now suppose during the game, the last four choices are RSPS. In other words, in the last round, the human chose paper and the computer chose scissors. The computer can predict that the human will most likely next choose scissors, since RSPSS appears more times (4) than RSPSR (1, predict rock) and RSPSP (3, predict paper) in the stored frequencies. Therefore, the computer should choose rock to beat the human's predicted choice of scissors. After the human makes a choice, update the appropriate frequency RPS Game with simple machine learning Human players of the Rock Paper Scissors game try to develop strategies to beat their opponents. Therefore, humans generally do not make random choices. Instead, their choices exhibit patterns that a computer can discover and exploit using a simple machine learning algorithm The computer's ML choice algorithm Continuously record the last N choices between the human and the computer player. Throw out the oldest choice in order to add a new one. For example, suppose N = 5 and during the game, the recorded choices are (the human's choices are underlined): PSRSP The last choice was made by the human, and it was paper. For each recorded sequence that ends with the human's choice, the computer should store how many times that sequence has occurred (each sequence's frequency). For example, for N 5, some of the stored sequences and their frequencies may be (in no particular order, the human choices are underlined) Now suppose during the game, the last four choices are RSPS. In other words, in the last round, the human chose paper and the computer chose scissors. The computer can predict that the human will most likely next choose scissors, since RSPSS appears more times (4) than RSPSR (1, predict rock) and RSPSP (3, predict paper) in the stored frequencies. Therefore, the computer should choose rock to beat the human's predicted choice of scissors. After the human makes a choice, update the appropriate frequency