def test_sarsa(iterations=1000, mlambda=None, n0=100, avg_it=50):
print "\n-------------------"
print "TD control Sarsa"
print "run for n. iterations: " + str(iterations)
print "plot graph mse vs episodes for lambda equal 0 and lambda equal 1"
print "list (standard output) win percentage for values of lambda 0, 0.1, 0.2, ..., 0.9, 1"
monte_carlo_Q = pickle.load(
open("Data/Qval_func_1000000_MC_control.pkl", "rb"))
n_elements = monte_carlo_Q.shape[0] * monte_carlo_Q.shape[1] * 2
mse = []
if not isinstance(mlambda, list):
# if no value is passed for lambda, default 0.5
l = 0.5 if mlambda == None else mlambda
# learn
game = Environment()
agent = Agent(game, n0)
agent.TD_control(iterations, l, avg_it)
# plot results
agent.show_statevalue_function()
else:
# test each value of lambda
for l in mlambda:
game = Environment()
agent = Agent(game, n0)
l_mse = agent.TD_control(iterations, l, avg_it)
mse.append(l_mse)
plt.plot(mlambda, mse)
plt.ylabel('mse')
plt.show()
def test_td_control(iter=50000):
lambdas = [0, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1.0]
for l in lambdas:
agent = Agent()
agent.TD_control(iter, l)
agent.plot_state()
