import numpy as np
from Players import Player
class QPlayer(Player):
# https://towardsdatascience.com/simple-reinforcement-learning-q-learning-fcddc4b6fe56
# https://www.learndatasci.com/tutorials/reinforcement-q-learning-scratch-python-openai-gym/
def __init__(self,
stack=10_000,
count=0,
eps=0.2,
alpha=0.1,
gamma=0.9,
load=False,
update=True):
Player.__init__(self, name='Q', stack=stack, count=count)
self.eps = eps # percentage of exploration
self.alpha = alpha # learning rate
self.gamma = gamma # discount factor, typically 0.8-0.99
self.actions = [0, 1] # 0: Stand, 1: Hit
self.offset = 3
self.load = load
self.update = update
if self.load:
self.Q = np.load(self.load)
else:
self.Q = np.zeros((10, 20, 2))
def ExploreOrExploit(self):
d, t = self.GetState()
if np.random.uniform(0., 1.) < self.eps:
def __init__(self, symbol):
Player.__init__(self, symbol)