N armed bandit simulation
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As i learn more and more about machine learning and AI algorithms in general, i came across this book by Sutton & Barto, which is all about reinforcement learning. As i read through the action-value topic, it seemed a nice R-challenge, so here’s the complete code:
To test one greedy player and two epsilon players, for example, just rundo.simulation(N = 500, plays = 1000, eps = c(0, 0.01, 0.1))A quick explanation of some key parameters:
- N: the number of replications for each simulation
- plays: the number of moves for each player
- eps: the probability of make a exploratory (random) move, for each player
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