simab - Simple Multi-Armed Bandit Simulator
Algorithms
- Epsilon First
- Epsilon Greedy
- Softmax
- UCB1
- Random: randomly choose an arm
- Oracle: always choose the best arm
- Single: keep choosing a specific arm
Arms
- Normal Distribution
- Gaussian Mixture Model
Installation
$ pip install simabUsage
Basic
from simab.arms.normal import NormalArm
from simab.algorithms.epsilon_first import EpsilonFirst
from simab.algorithms.epsilon_greedy import EpsilonGreedy
from simab.algorithms.oracle import Oracle
from simab.algorithms.random_choice import Random
from simab.algorithms.single_arm import Single
from simab.algorithms.softmax import Softmax
from simab.algorithms.ucb1 import UCB1
ROUNDS = 1000
# Generate five ND-arms.
arms = [NormalArm(0.1*float(i), 0.1) for i in range(3, 8)]
# Generate an agent.
algorithm = Softmax(arms, 0.1)
# algorithm = EpsilonFirst(arms, 0.1, rounds=ROUNDS)
# algorithm = EpsilonGreedy(arms, 0.1)
# algorithm = UCB1(arms)
# algorithm = Single(arms, 2)
# algorithm = Random(arms)
for _ in range(ROUNDS):
algorithm.play()
# Get the summary.
summary = algorithm.summary()
print summary['algorithm']
print summary['tau']
print summary['plays']
print summary['total_reward']
print summary['true_means']
print summary['true_sds']
print summary['empirical_means']
print summary['empirical_sds']
print summary['history']Predicted(Simulated) MAB
You need to generate reward from each arm at each round at first if you conduct simulation with Oracle.
ROUNDS = 1000
arms = [NormalArm(0.1*float(i), 0.1) for i in range(3, 8)]
# Generate predictions
for arm in arms:
arm.predict(1000)
algorithm = Oracle(arms)
for _ in range(ROUNDS):
algorithm.play()If you want to simulate algorithms and compare them to Oracle, it's better use the same arms yielding exactly the same reward for each round. In such situations, you can simply reset() arms to the initial states while they keep predictions.
ROUNDS = 1000
arms = [NormalArm(0.1*float(i), 0.1) for i in range(3, 8)]
for arm in arms:
arm.predict(1000)
for algorithm in [Softmax(arms, 0.1), EpsilonFirst(arms, 0.1, rounds=ROUNDS), EpsilonGreedy(arms, 0.1), UCB1(arms), Single(arms, 2), Random(arms)]:
for _ in range(ROUNDS):
algorithm.play()
for arm in arms:
arm.reset()
