Snippet for toyCarIRL, blog usage, not executable

toyCarIRL.py

# IRL algorith developed for the toy car obstacle avoidance problem for testing.
import numpy as np
import logging
import scipy
from playing import play #get the RL Test agent, gives out feature expectations after 2000 frames
from nn import neural_net #construct the nn and send to playing
from cvxopt import matrix 
from cvxopt import solvers #convex optimization library
from flat_game import carmunk # get the environment
from learning import IRL_helper # get the Reinforcement learner

NUM_STATES = 8 
BEHAVIOR = 'red' # yellow/brown/red
FRAMES = 100000 # number of RL training frames per iteration of IRL