from MonteCarlo import MonteCarlo from TDLambda import TDLambda from LinFuncApprox import LinFuncApprox import matplotlib.pyplot as plt from plotQValues import plotQValues '''Monte Carlo''' mc = MonteCarlo() q_mc, n_mc = mc.run_episodes(10000) # plotQValues(q_mc) '''TD Lambda''' # td = TDLambda(0.5) # td.run_episodes(100000) # q_td = td.q_values() # plotQValues(q_td) # # mse = [] # lmbda = [] # for i in range(0, 10): # lmbda.append(i / 10.) # td = TDLambda(i / 10.) # q_td, n_td = td.run_episodes(10000) # # error = (q_td - q_mc) ** 2 # mse.append(sum(sum(sum(error * 1./ (2 * 21 * 10))))) # # plt.plot(lmbda, mse) # # plt.show() '''Linear Function Approximation''' # lin = LinFuncApprox() # lin.run_episodes(10000)
