def sarsa_lambda_gradient_descent():
import matplotlib.pyplot as plt
plt.ion()
import q_learning as q
import numpy as np
from copy import deepcopy
reload(q)
game = q.MountainCarGame
tile_in_row = 9
n_tilings = 5
#dot = sum(initial_theta[phi((2,2))])
state_adapter = q.mountain_car_game_tilings_state_adapter(n_tilings, tile_in_row)
state_adapter2 = lambda s: np.array(state_adapter(s))
initial_theta = np.array([1] * tile_in_row * tile_in_row * n_tilings)
q_algo1 = q.SARSALambdaGradientDescent(game().get_actions(), game().get_state(),
initial_q = 0, initial_theta = initial_theta, state_adapter = state_adapter2)
q_algo1.epsilon = 0.02
q_algo1.lmbda = 0.5
q_algo1.gamma = 0.9
q_algo1.alpha = 0.1
teacher = q.Teacher(game, q_algo1, q.MountainCarGameVisualizer(q_algo1))
teacher.teach(1)
teacher = q.Teacher(game, q_algo1, q.GameNoVisualizer())
teacher.teach(30)
def sarsa_lambda_on_mountain_car_game():
import q_learning as q
import numpy as np
from copy import deepcopy
reload(q)
game = q.MountainCarGame
state_adapter = q.mountain_car_game_tilings_state_adapter(tile_in_row=9,
n_tilings=5)
q_algo1 = q.SARSALambda(game().get_actions(),
game().get_state(),
0,
memory_size=40,
state_adapter=state_adapter)
q_algo1.epsilon = 0.2
q_algo1.lmbda = 0.9
q_algo1.gamma = 0.5
visualizer = q.MountainCarGameVisualizer(q_algo1)
teacher = q.Teacher(game, q_algo1, visualizer)
teacher.teach(1)
teacher = q.Teacher(game, q_algo1, q.GameNoVisualizer())
teacher.teach(30)
def sarsa_lambda_on_mountain_car_game(): import q_learning as q import numpy as np from copy import deepcopy reload(q) game = q.MountainCarGame state_adapter = q.mountain_car_game_tilings_state_adapter(tile_in_row = 9, n_tilings = 5) q_algo1 = q.SARSALambda(game().get_actions(), game().get_state(), 0, memory_size = 40, state_adapter = state_adapter) q_algo1.epsilon = 0.2 q_algo1.lmbda = 0.9 q_algo1.gamma = 0.5 visualizer = q.MountainCarGameVisualizer(q_algo1) teacher = q.Teacher(game, q_algo1, visualizer) teacher.teach(1) teacher = q.Teacher(game, q_algo1, q.GameNoVisualizer()) teacher.teach(30)
def sarsa_lambda_gradient_descent():
import matplotlib.pyplot as plt
plt.ion()
import q_learning as q
import numpy as np
from copy import deepcopy
reload(q)
game = q.MountainCarGame
tile_in_row = 9
n_tilings = 5
#dot = sum(initial_theta[phi((2,2))])
state_adapter = q.mountain_car_game_tilings_state_adapter(
n_tilings, tile_in_row)
state_adapter2 = lambda s: np.array(state_adapter(s))
initial_theta = np.array([1] * tile_in_row * tile_in_row * n_tilings)
q_algo1 = q.SARSALambdaGradientDescent(game().get_actions(),
game().get_state(),
initial_q=0,
initial_theta=initial_theta,
state_adapter=state_adapter2)
q_algo1.epsilon = 0.02
q_algo1.lmbda = 0.5
q_algo1.gamma = 0.9
q_algo1.alpha = 0.1
teacher = q.Teacher(game, q_algo1, q.MountainCarGameVisualizer(q_algo1))
teacher.teach(1)
teacher = q.Teacher(game, q_algo1, q.GameNoVisualizer())
teacher.teach(30)
