# Training to prevent errors arising from connected training
agent = w_QAgent(self.opt_env)
agent.qlearn(3500)
rews = utility(agent)
x = max(rews, self.max_reward)
return (vector, x)
if __name__ == "__main__":
max_layer = int(sys.argv[1])
num_iters = int(sys.argv[2])
env = WindyGridworld()
tree = Tree(env, max_layer)
tree.threshold = float(sys.argv[3])
tree.initialize()
tree.BFS(num_iters)
# Store data
r_dir = os.path.abspath(os.pardir)
data_dir = os.path.join(r_dir, "data-wgr")
txt_dir = os.path.join(data_dir,
"bfs_result_{}.txt".format(tree.max_layer))
a = tree.best_observed_choice()
import gym # pylint: disable=import-error
import time
from collections import deque
import csv
import ast
from sklearn.model_selection import train_test_split # pylint: disable=import-error
from wgrenv import WindyGridworld
from w_qlearn import w_QAgent
import matplotlib.pyplot as plt # pylint: disable=import-error
from termcolor import colored # pylint: disable=import-error
import multiprocessing as mp
from multiprocessing import Manager
import itertools
env = WindyGridworld() # reference environment
input_data = []
output_data = []
def make_env(env, mod_seq):
ref_env = copy.deepcopy(env)
locations = mod_seq
for element in locations:
if element[0] == 0:
ref_env.jump_cells.append((element[1], element[2]))
else:
ref_env.special.append((element[1], element[2]))
return ref_env
if show:
self.env.render()
return [steps, total, states]
def print_eval_result(self, output):
# Print results from evaluation for visualization
print("Steps taken: {}".format(output[0]))
print("Total reward: {}".format(output[1]))
print("States traversed: {}".format(output[2]))
return
if __name__ == "__main__":
env = WindyGridworld()
modified = copy.deepcopy(env)
modified.jump_cells.append((4, 2))
modified.special.append((3, 1))
modified.special.append((3, 4))
modified.special.append((3, 5))
start = time.time()
agent = w_QAgent(modified)
agent.qlearn(3000, render=False)
end = time.time()
series = env.resettable_states()
vals = []