def test_linear_sarsa(iterations=1000, mlambda=None, n0=100, avg_it=100):
print "\n-------------------"
print "TD control Sarsa, with Linear function approximation"
print "run for n. iterations: "+str(iterations)
print "plot graph mse vs episodes for lambda equal 0 and lambda equal 1"
print "list (std 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_linear(iterations,l,avg_it)
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_linear(iterations,l,avg_it)
mse.append(l_mse)
plt.plot(mlambda,mse)
plt.ylabel('mse')
plt.show()
def test_linear_sarsa(iterations=1000, mlambda=None, n0=100, avg_it=100):
print "\n-------------------"
print "TD control Sarsa, with Linear function approximation"
print "run for n. iterations: " + str(iterations)
print "plot graph mse vs episodes for lambda equal 0 and lambda equal 1"
print "list (std 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_linear(iterations, l, avg_it)
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_linear(iterations, l, avg_it)
mse.append(l_mse)
plt.plot(mlambda, mse)
plt.ylabel('mse')
plt.show()
def test_monte_carlo(iterations=1000000, n0=100):
print "\n-------------------"
print "Monte Carlo control"
print "run for n. iterations: "+str(iterations)
print "win percentage: "
# learn
game = Environment()
agent = Agent(game, n0)
agent.MC_control(iterations)
# plot and store
agent.show_statevalue_function()
agent.store_Qvalue_function()
def test_monte_carlo(iterations=1000000, n0=100):
print "\n-------------------"
print "Monte Carlo control"
print "run for n. iterations: " + str(iterations)
print "win percentage: "
# learn
game = Environment()
agent = Agent(game, n0)
agent.MC_control(iterations)
# plot and store
agent.show_statevalue_function()
agent.store_Qvalue_function()
