def main():
nProcess = multi.cpu_count()
name = ["MCSteps", "SSteps", "Qsteps"]# Change this!
algQs=list()
algrews=list()
steps = [250000] * 4
for i, agent in enumerate([MonteCarlo(env_in=EZ21()), SARSA(env_in=EZ21()), QLearn(env_in=EZ21())]):
print(name[i])
Qs = list()
algrews.append([])
run_sum = 0
for eps in steps:
run_sum += eps
print(run_sum)
agent.n = eps
agent.iter_opt()
Qs.append(deepcopy(agent.Q))
with multi.Pool(nProcess) as pool:
algrews.append(pool.map(play_rounds, algQs[i]))
with open(name[i] + "_algQs", 'wb') as myfile:
pickle.dump(algQs, myfile)
with open(name[i] + "_algrews", 'wb') as myfile:
pickle.dump(algrews, myfile)
return
def play_rounds(Q_in):
env = EZ21()
results = list()
for i in range(250000):
state = tuple(env.reset())
while True:
act = greedy_pol(Q_in, state)
_state, rew, term = env.step(act) # Holding next state in buffer variable
if term:
results.append(rew)
break
state = tuple(_state)
return results
import seaborn as sns
import pandas as pd
from decimal import Decimal
from sarsa import SARSA
from q_learn import QLearn
from matplotlib.lines import Line2D
with open(r'C:\Source_files\Python\Pantry\MCQs', "rb") as f:
MQs = pickle.load(f)
with open(r'C:\Source_files\Python\Pantry\SARSA_Qs', "rb") as f:
SARSAQs = pickle.load(f)
with open(r'C:\Source_files\Python\BBB\QLong_Qs', "rb") as f:
QLearnQs = pickle.load(f)
Qs = zip(MQs, SARSAQs, QLearnQs)
MC = MonteCarlo(env_in=EZ21())
SRS = SARSA(env_in=EZ21())
QL = QLearn(env_in=EZ21())
MCrews = list()
SARSArews = list()
QRews = list()
for j, (M, S, L) in enumerate(Qs):
if j >= 50:
break
print(j)
MCrews.append([])
SARSArews.append([])
QRews.append([])
MC.reset(Q_in=M)
SRS.reset(Q_in=S)
QL.reset(Q_in=L)
from matplotlib import cm
from mpl_toolkits.mplot3d import Axes3D
import time
method = "MC"
with open(r'C:\Source_files\Python\BBB\MC100000_Qs', "rb") as f:
Qs = pickle.load(f)
with open(r'C:\Source_files\Python\Pantry\MCQs', "rb") as f:
Qs += tuple(pickle.load(f))
s1 = np.arange(10) + 1
s2 = np.arange(21) + 1
ss1, ss2 = np.meshgrid(s1, s2, indexing='ij')
EZ = EZ21()
MC = MonteCarlo(env_in=EZ)
fig = plt.figure(figsize=(10, 6))
V_MCs = list()
Pols = list()
Pol = []
V_MC = []
for j, Q in enumerate(Qs):
MC.reset(Q)
V_MC = np.zeros([10, 21])
Pol = np.zeros([10, 21])
for row in range(10):
for col in range(21):
V_MC[row, col] = MC.get_V((row + 1, col + 1))
Pol[row, col] = MC.greedy_pol((row + 1, col + 1))
V_MCs.append(V_MC)
def continue_Q(Q_in):
if "1" in multi.current_process().name:
print(('%s began working' % multi.current_process().name))
agent = QLearn(Q_in=Q_in, env_in=EZ21(), n_in=100000)
return agent.iter_opt(), agent.Q
def Monte(cnstnts=(None, None)):
if "1" in multi.current_process().name:
print(('%s began working' % multi.current_process().name))
agent = MonteCarlo(env_in=EZ21(), n_in=100000)
return agent.iter_opt(), agent.Q
def On_Pol(cnstnts=(None, None)):
if "1" in multi.current_process().name:
print(('%s began working' % multi.current_process().name))
agent = SARSA(env_in=EZ21(), n_in=80000, cnst_par=cnstnts)
return agent.iter_opt(), agent.Q
import numpy as np
import matplotlib.pyplot as plt
import pickle
from monte_hall_2 import MonteCarlo
from EZ21 import EZ21
import seaborn as sns
import pandas as pd
from decimal import Decimal
from sarsa import SARSA
from q_learn import QLearn
from matplotlib.lines import Line2D
with open(r'C:\Source_files\Python\BBB\Box_data', "rb") as f:
data = pickle.load(f)
MC = MonteCarlo(env_in=EZ21())
naive_rews = list()
for i in range(20000):
state = MC.env.reset()
while True:
state, rew, term = MC.env.step(MC.greedy_pol(tuple(state)))
if term:
naive_rews.append(rew)
break
legend_elements = [
Line2D([0], [0],
marker='o',
ls="None",
alpha=0.8,
label="Trained Policies",