steps_per_theta_update=None,
verbose=1,
norm_value=NORM_VALUE,
independent=INDEPENDENT)
# term_condition=lambda v1, v2:
# increment_base_termination(v1,v2,2,tol=1e-2))
#term_condition=lambda v1, v2: terminal_evaluation(v1,v2,1e-1))
def tmetric(theta):
t = pbo.apply_bo(theta[0], n_times=STEPS_AHEAD)
return q_regressor.get_k(t)
state, actions, reward, next_states, absorbing = split_dataset(dataset,
state_dim=state_dim,
action_dim=action_dim,
reward_dim=reward_dim)
theta0 = np.array([6., 10.001], dtype='float32').reshape(1, -1)
# theta0 = np.array([16., 10.001], dtype='float32').reshape(1, -1)
history = pbo.fit(state, actions, next_states, reward, absorbing, theta0,
batch_size=10, nb_epoch=EPOCH,
theta_metrics={'k': tmetric})
##########################################
# Evaluate the final solution
initial_states = np.array([[1, 2, 5, 7, 10]]).T
values = evaluation.evaluate_policy(mdp, pbo, initial_states=initial_states)
print('Learned theta: {}'.format(pbo.learned_theta_value))
print('Final performance of PBO: {}'.format(values))
'scale': True,
'verbose': 1,
'significance': args.significance
}
selector = IFS(**ifs_params)
features_names = np.array(['S%s' % i for i in xrange(state_dim)] +
['A%s' % i for i in xrange(action_dim)])
rfs_params = {
'feature_selector': selector,
'features_names': features_names,
'verbose': 1
}
fs = RFS(**rfs_params)
# Split dataset for RFS
state, actions, reward, next_states = split_dataset(
dataset, state_dim, action_dim, reward_dim)
# Run RFS
fs.fit(state, actions, next_states, reward)
# Reduce the dataset for FQI
selected_states = []
selected_actions = []
for f in features_names[np.where(fs.get_support())]:
if f.startswith('S'):
selected_states.append(f)
if f.startswith('A'):
selected_actions.append(f)
# TODO remove this once everything works
assert len(selected_states) > 0, '### RFS fail ###'
# dataset: s, a, r, s'
# dataset = evaluation.collect_episodes(mdp, n_episodes=50)
dataset = np.loadtxt('encoded_dataset.csv', skiprows=1, delimiter=',')
# check_dataset(dataset, state_dim, action_dim, reward_dim)
estimator = ExtraTreesRegressor(n_estimators=50, n_jobs=-1,
importance_criterion="gini")
# estimator = DecisionTreeRegressor(importance_criterion="gini")
selector = IFS(estimator=estimator,
scale=True, verbose=1)
features_names = ['S%s' % i for i in xrange(state_dim)] + ['A%s' % i for i in
xrange(action_dim)]
fs = RFS(feature_selector=selector,
# features_names=np.array(['S0', 'S1', 'S2', 'S3', 'A0', 'A1']),
features_names=np.array(features_names),
verbose=1)
state, actions, reward, next_states = \
split_dataset(dataset, state_dim, action_dim, reward_dim)
state = dataset[:,0:state_dim]
actions = dataset[:,state_dim:state_dim+action_dim]
reward = dataset[:,state_dim+action_dim]
# print(dataset[:10, :])
fs.fit(state, actions, next_states, reward)
print(
fs.get_support()) # this are the selected features, it should be [s0, s2, a0]
update_theta_every=UPDATE_EVERY,
steps_per_theta_update=None,
verbose=1,
norm_value=NORM_VALUE,
independent=INDEPENDENT)
# term_condition=lambda v1, v2:
# increment_base_termination(v1,v2,2,tol=1e-2))
#term_condition=lambda v1, v2: terminal_evaluation(v1,v2,1e-1))
def tmetric(theta):
t = pbo.apply_bo(theta[0], n_times=STEPS_AHEAD)
return q_regressor.get_k(t)
state, actions, reward, next_states, absorbing = split_dataset(
dataset, state_dim=state_dim, action_dim=action_dim, reward_dim=reward_dim)
theta0 = np.array([6., 10.001], dtype='float32').reshape(1, -1)
# theta0 = np.array([16., 10.001], dtype='float32').reshape(1, -1)
history = pbo.fit(state,
actions,
next_states,
reward,
absorbing,
theta0,
batch_size=10,
nb_epoch=EPOCH,
theta_metrics={'k': tmetric})
##########################################
# Evaluate the final solution
# np.random.seed(3452)
mdp = env.SyntheticToyFS()
state_dim, action_dim, reward_dim = get_space_info(mdp)
nextstate_idx = state_dim + action_dim + reward_dim
reward_idx = action_dim + state_dim
# dataset: s, a, r, s'
dataset = evaluation.collect_episodes(mdp, n_episodes=50)
check_dataset(dataset, state_dim, action_dim, reward_dim)
selector = IFS(estimator=ExtraTreesRegressor(n_estimators=50),
scale=True, verbose=1)
fs = RFS(feature_selector=selector,
features_names=np.array(['S0', 'S1', 'S2', 'S3', 'A0', 'A1']),
verbose=1)
state, actions, reward, next_states, absorbing = \
split_dataset(dataset, state_dim, action_dim, reward_dim)
# print(dataset[:10, :])
fs.fit(state, actions, next_states, reward)
selected_features = fs.features_names[fs.get_support()]
print('selected features: {}'.format(selected_features)) # this are the selected features, it should be [s0, s2, a0]
assert np.all(selected_features == ['S0', 'S2', 'A0'])
print(fs.nodes)
g = fs.export_graphviz()
g.view()
'n_jobs': args.njobs}
ifs_params = {'estimator': ExtraTreesRegressor(**ifs_regressor_params),
'n_features_step': 1,
'cv': None,
'scale': True,
'verbose': 1,
'significance': args.significance}
selector = IFS(**ifs_params)
features_names = np.array(['S%s' % i for i in xrange(state_dim)] + ['A%s' % i for i in xrange(action_dim)])
rfs_params = {'feature_selector': selector,
'features_names': features_names,
'verbose': 1}
fs = RFS(**rfs_params)
# Split dataset for RFS
state, actions, reward, next_states = split_dataset(dataset, state_dim, action_dim, reward_dim)
# Run RFS
fs.fit(state, actions, next_states, reward)
# Reduce the dataset for FQI
selected_states = []
selected_actions = []
for f in features_names[np.where(fs.get_support())]:
if f.startswith('S'):
selected_states.append(f)
if f.startswith('A'):
selected_actions.append(f)
# TODO remove this once everything works
assert len(selected_states) > 0, '### RFS fail ###'