def example_randomwalk():
""" An example on random walk MDP """
# create an MDP
env = RandomWalk(19, -1)
# create n-step TreeBackup agent
agent = TreeBackup(env, env.init(), 3)
agent2 = TreeBackup(env, env.init(), 3)
# act using equiprobable random policy with discount = 0.9 and step size = 0.1
num_episode = 1000
for iter in range(num_episode):
agent.episode(0.9, 0.1)
agent.init()
agent2.set_policy_eps_greedy(0.1)
for iter in range(num_episode):
agent2.episode(0.9, 0.1)
agent2.init()
print('Q_DP[s][a] ', env.Q_equiprobable(0.9))
print('Q_eps_greedy[s][a] ', env.Q_eps_greedy(0.1, 0.9))
print('Equiprobable Q_TreeBackup[s][a]', agent.Q)
print('Eps greedy Q_TreeBackup[s][a]', agent2.Q)
def example_randomwalk():
""" An example on random walk MDP """
# create an MDP
env = RandomWalk(19, -1)
# create n-step QSigma agent
agent = QSigma(env, 0.5, env.init(), 3) #Psigma=0.5, init_state=env.init(), steps=3
agent2 = QSigma(env, 0.5, env.init(), 3)
# act using equiprobable random policy with discount = 0.9 and step size = 0.1
num_episode = 1000
for iter in range(num_episode):
agent.episode(0.9, 0.1)
agent.init()
agent2.set_policy_eps_greedy(0.1)
for iter in range(num_episode):
agent2.episode(0.9, 0.1)
agent2.init()
print('Q_DP[s][a] ', env.Q_equiprobable(0.9))
print('Q_eps_greedy[s][a] ', env.Q_eps_greedy(0.1, 0.9))
print('Equiprobable Q_Q(sigma)[s][a]', agent.Q)
print('Eps greedy Q_Q(sigma)[s][a]', agent2.Q)
