def perfect_features_test():
from src.env.Amatrix_task import Amatrix
n = 20
m = 3
env = Amatrix(n, m)
features = env.Amatrix # perfect features
weights = np.random.rand(n)
config = Config()
config.parameter_size = n
config.init_alpha = 0.001
adam = Adam(config)
sample_size = 100000
for i in range(sample_size):
rand_row = np.random.randint(n)
target = env.sample_target(rand_row, noisy=True)
pred_features = features[rand_row, :]
prediction = np.dot(pred_features, weights)
error = target - prediction
gradient, new_stepsize, new_weight_vector = adam.update_weight_vector(
error, pred_features, weights)
weights = new_weight_vector
if (i + 1) % 10000 == 0:
print("Sample number: {0}".format(i + 1))
print("\tPrediction error:{0}".format(error))
print("Theta star:\n{0}".format(env.theta_star))
print("Estimated theta:\n{0}".format(weights))
difference = np.sqrt(np.sum(np.square(env.theta_star - weights)))
print("L2 norm of difference:\n{0}".format(difference))
def imperfect_features_test():
from src.env.Amatrix_task import Amatrix
n = 4
m = 2
env = Amatrix(n, m)
features = env.get_approx_A() # first m features
weights = np.random.rand(m)
config = Config()
config.parameter_size = m
config.init_alpha = 0.001
adam = Adam(config)
sample_size = 50000
for i in range(sample_size):
rand_row = np.random.randint(n)
target = env.sample_target(rand_row, noisy=True)
pred_features = features[rand_row, :]
prediction = np.dot(pred_features, weights)
error = target - prediction
gradient, new_stepsize, new_weight_vector = adam.update_weight_vector(
error, pred_features, weights)
weights = new_weight_vector
print("Sample number: {0}".format(i + 1))
print("\tPrediction error:{0}".format(error))
print("Theta star:\n{0}".format(env.theta_star))
print("Estimated theta:\n{0}".format(weights))
def adding_bad_features_test():
from src.env.Amatrix_task import Amatrix
n = 10
m = 5
env = Amatrix(n, m)
features = env.get_approx_A() # first m features
weights = np.zeros(m)
config = Config()
config.parameter_size = m
config.theta = 0.1
config.init_beta = np.log(0.0001)
idbd = SIDBD(config)
sample_size = 50000
additional_features = 30
for k in range(additional_features + 1):
print("Number of features in the representation: {0}".format(
idbd.parameter_size))
for i in range(sample_size):
rand_row = np.random.randint(n)
target = env.sample_target(rand_row, noisy=True)
pred_features = features[rand_row, :]
prediction = np.dot(pred_features, weights)
error = target - prediction
gradient, new_stepsize, new_weight_vector = idbd.update_weight_vector(
error, pred_features, weights)
weights = new_weight_vector
if ((i + 1) % 25000) == 0:
print("\tSample number: {0}".format(i + 1))
print("\t\tPrediction error: {0}".format(error))
print("Theta star:\n{0}".format(env.theta_star))
print("Estimated theta:\n{0}".format(weights))
if k < additional_features:
print("Adding new feature...")
new_feature = env.get_new_bad_features(1)
features = np.hstack((features, new_feature))
idbd.increase_size(1)
new_weights = np.zeros(m + 1)
new_weights[:m] = weights
m += 1
weights = new_weights
def test_function_approximator(num_features=20,
initial_features=20,
num_iterations=10000,
chkpt=100,
plot_mse=True,
noisy=True,
add_features=False,
add_true_features=True,
feature_add_interval=100,
mixed_features=False):
from src.step_size_methods import SGD
config = Config()
# task setup
config.num_true_features = num_features
config.num_obs_features = initial_features # same as function approximator
config.max_num_features = 20000 # same as function approximator
task = RandomFeatures(config)
# function approximator setup
approximator = LinearFunctionApproximator(config)
# optimizer setup
config.parameter_size = initial_features
config.alpha = 0.001
optimizer = SGD(config)
# for plotting
mse_per_chpt = np.zeros(num_iterations // chkpt, dtype=np.float64)
mse = 0
current_chpt = 0
# training loop
for i in range(num_iterations):
target, observable_features, best_approximation = task.sample_observation(
noisy=noisy)
prediction = approximator.get_prediction(observable_features)
error = target - prediction
_, _, new_weights = optimizer.update_weight_vector(
error, observable_features, approximator.get_weight_vector())
approximator.update_weight_vector(new_weights)
squared_loss = np.square(error)
mse += squared_loss / chkpt
if (i + 1) % chkpt == 0:
# reporting and saving
print("Iteration number: {0}".format(i + 1))
print("\tTarget: {0:.4f}".format(target))
print("\tPrediction: {0:.4f}".format(prediction))
print("\tMean Squared Error: {0:.4f}".format(mse))
mse_per_chpt[current_chpt] += mse
mse *= 0
current_chpt += 1
if add_features and (i + 1) % feature_add_interval == 0:
task.add_new_feature(k=1, true_feature=add_true_features)
approximator.increase_num_features(k=1)
optimizer.increase_size(k=1)
if mixed_features:
add_true_features = not add_true_features
if plot_mse:
# plots
import matplotlib.pyplot as plt
x_axis = np.arange(num_iterations // chkpt)
plt.plot(x_axis, mse_per_chpt)
plt.show()
plt.close()
def boyan_chain_test(steps=50000):
from src.env.BoyanChain import BoyanChain
from src.env.RandomFeatures_task import LinearFunctionApproximator
from src.util import Config
import matplotlib.pyplot as plt
config = Config()
checkpoint = 100
""" Environment Setup """
config.init_noise_var = 0.1
config.num_obs_features = 4
config.max_num_features = 9
""" AutoTIDBD Setup """
config.parameter_size = 4
config.theta = 0.001
config.tau = 10000
config.init_stepsize = 0.001
# to keep track of learning progress
run_avg_msve = np.zeros(steps // checkpoint, dtype=np.float64)
current_checkpoint = 0
avg_msve = 0
env = BoyanChain(config)
approximator = LinearFunctionApproximator(config)
optimizer = AutoTIDBD(config)
""" Start of Learning"""
curr_obs_feats = env.get_observable_features()
for s in range(steps):
state_value = approximator.get_prediction(curr_obs_feats)
optimal_value = env.compute_true_value()
# step in the environment
_, r, next_obs_feats, term = env.step()
next_state_value = approximator.get_prediction(next_obs_feats)
# compute td error
td_error = r + (1 - term) * next_state_value - state_value
# update weights
_, _, new_weights = optimizer.update_weight_vector(
td_error,
features=curr_obs_feats,
weights=approximator.get_weight_vector(),
discounted_next_features=next_obs_feats)
approximator.update_weight_vector(new_weights)
# update features
curr_obs_feats = next_obs_feats
# keep track of progress
avg_msve += np.square(state_value - optimal_value) / checkpoint
# check if terminal state
if term:
env.reset()
curr_obs_feats = env.get_observable_features()
# store learning progress so far
if (s + 1) % checkpoint == 0:
run_avg_msve[current_checkpoint] += avg_msve
avg_msve *= 0
current_checkpoint += 1
if (s + 1) == (steps // 2):
env.add_feature(k=4, noise=0.0, fake_feature=False)
approximator.increase_num_features(4)
optimizer.increase_size(4)
curr_obs_feats = env.get_observable_features()
print("The average MSVE is: {0:0.4f}".format(np.average(run_avg_msve)))
xaxis = np.arange(run_avg_msve.size) + 1
plt.plot(xaxis, run_avg_msve)
plt.show()
plt.close()
def sarsa_zero_test(steps=10000,
add_new_centers=False,
number_of_irrelevant_features=0):
import matplotlib.pyplot as plt
from src.env.RandomFeatures_task import LinearFunctionApproximator
from src.step_size_methods.sgd import SGD
# epsilon greedy policy
def choose_action(av_array: np.ndarray, epsilon):
p = np.random.rand()
if p > epsilon:
argmax_av = np.random.choice(
np.flatnonzero(av_array == av_array.max()))
return argmax_av
else:
return np.random.randint(av_array.size)
# for computing action values
def get_action_values(n, features, approximator_list):
action_values = np.zeros(n, dtype=np.float64)
for k in range(n):
action_values[k] += approximator_list[k].get_prediction(features)
return action_values
completed_episodes_per_run = []
for _ in range(1):
print("==== Results for Sarsa(0) with Epsilon Greedy Policy ====")
config = Config()
# setting up feature function
config.state_dims = 2
config.state_lims = np.array(((-1, 1), (-1, 1)), dtype=np.float64)
# config.initial_centers = np.array(((0.0,0.0), (-1.8,0), (1.8,0), (0.0,-1.8), (0.0,1.8)), dtype=np.float64)
config.initial_centers = np.array(
((0.0, 0.0), (0.25, 0.25), (0.25, -0.25), (-0.25, -0.25),
(-0.25, 0.25)),
dtype=np.float64)
config.sigma = 0.5
config.init_noise_mean = 0.0
config.init_noise_var = 0.01
feature_function = RadialBasisFunction(config)
# setting up environment
config.norm_state = True
env = MountainCar(config)
# function approximator and optimizer parameters
num_actions = 3
random_action_prob = 0.1
gamma = 0.99
config.num_obs_features = feature_function.num_features
config.max_num_features = 200 # as long as this is more than 12
config.num_actions = num_actions
config.alpha = 0.005
config.rescale = False
config.parameter_size = feature_function.num_features
function_approximator = []
optimizer = []
# one instance for each action
for i in range(num_actions):
function_approximator.append(LinearFunctionApproximator(config))
optimizer.append(SGD(config))
# setting up summaries
all_episodes_return = []
episode_return = 0
# setting up initial state, action, features, and action values
curr_s = env.get_current_state()
curr_features = feature_function.get_observable_features(curr_s)
curr_avs = get_action_values(num_actions, curr_features,
function_approximator)
curr_a = choose_action(curr_avs, random_action_prob)
midpoint_episode = 0
for i in range(steps):
# get current action values
curr_avs = get_action_values(num_actions, curr_features,
function_approximator)
# execute current action
next_s, r, terminal = env.step(curr_a)
next_features = feature_function.get_observable_features(next_s)
# get next action values and action
next_action_values = get_action_values(num_actions, next_features,
function_approximator)
next_action = choose_action(next_action_values, random_action_prob)
# compute TD error for Sarsa(0)
td_error = r + gamma * (
1 -
terminal) * next_action_values[next_action] - curr_avs[curr_a]
# update weight vector
_, ss, new_weights = optimizer[curr_a].update_weight_vector(
td_error, curr_features,
function_approximator[curr_a].get_weight_vector())
function_approximator[curr_a].update_weight_vector(new_weights)
# set current features and action
curr_features = next_features
curr_a = next_action
# keep track of sum of rewards
episode_return += r
# if terminal state
if terminal:
env.reset()
all_episodes_return.append(episode_return)
episode_return *= 0
curr_s = env.get_current_state()
curr_features = feature_function.get_observable_features(
curr_s)
curr_avs = get_action_values(num_actions, curr_features,
function_approximator)
curr_a = choose_action(curr_avs, random_action_prob)
# if midpoint of training
if (i + 1) == (steps // 2):
if add_new_centers:
new_centers = np.array(
((0, 0), (0.25, 0.25), (0.25, -0.25), (-0.25, -0.25),
(-0.25, 0.25)),
dtype=np.float64)
feature_function.add_centers(new_centers,
noise_var=0,
noise_mean=0)
for k in range(num_actions):
function_approximator[k].increase_num_features(
new_centers.shape[0])
optimizer[k].increase_size(new_centers.shape[0],
init_stepsize=0.25)
if number_of_irrelevant_features > 0:
new_feature_mean = 0.0
new_feature_var = 0.05
fake_features = True
feature_function.add_feature(number_of_irrelevant_features,
noise_mean=new_feature_mean,
noise_var=new_feature_var,
fake_feature=fake_features)
for k in range(num_actions):
function_approximator[k].increase_num_features(
number_of_irrelevant_features)
optimizer[k].increase_size(
number_of_irrelevant_features)
curr_features = feature_function.get_observable_features(
curr_s)
midpoint_episode = len(all_episodes_return)
completed_episodes_per_run.append(len(all_episodes_return))
print("Number of episodes completed: {0}".format(
len(all_episodes_return)))
print("Average episodes completed: {0:0.4f}".format(
np.average(completed_episodes_per_run)))
print("Return per episode:\n", all_episodes_return)
plt.plot(np.arange(len(all_episodes_return)) + 1, all_episodes_return)
plt.vlines(x=midpoint_episode, ymin=-800, ymax=0)
plt.ylim((-800, 0))
plt.show()
plt.close()
