Multi-armed Bandit Algorithms in Golang.
This is the algorithm to solve multi-armed bandit problem.
https://en.wikipedia.org/wiki/Multi-armed_bandit
Though there are a lot of algorithms to solve this problem, I implemented UCB1 and epsilon-greedy strategy one so far.
To be simple, I suppose that all of success probabilities of arms will follow a bernoulli distribution.
$ go build main.go
$ ./main -s=[FilePath] -i=[Number]
- -s =
- file path for prob.d
- -i =
- 0 : Epsilon Greedy,
- 1 : UCB1,
- 2 : Softmax,
- 3 : Thompson Sampling
e.g,
$ ./main -s=./arms/prob.d -i=1
You can also modify the number of trials and the maximum number of arms. (They are defined in main.go as constant.)
If you want to add another arm, you need to modify "./arms/prob.d" and add success probability of the arm.
------------------------------
Total Trials: 99
Selected arm: 1
Reward: 1
[{Succecc probability, Number of selected}] = [{0.2 18} {0.4 28} {0.8 36} {0.1 18}]
------------------------------
| Arms | Success Probability | Selected Num |
|---|---|---|
| Arm A | 0.2 | 18 |
| Arm B | 0.4 | 28 |
| Arm C | 0.8 | 36 |
| Arm D | 0.1 | 18 |
Using a pseudo-random number generator by leesper
https://github.com/leesper/go_rng