def main(workers = 0,
k = 36,
encoding = 'tile',
env_size = 9,
n_runs = 1,
n_reward_samples = 2500,
n_reward_runs = 25,
lam = 0.,
gam = 0.995,
beta = 0.995,
alpha = 1.,
eta = 0.5,
eps = 1e-5,
patience = 16,
max_iter = 8,
l1theta = None,
l1code = None,
l2code = None,
n_samples = None,
nonlin = None,
nonzero = None,
training_methods = None,
min_imp = 0.0001,
min_delta = 1e-6,
fldir = '/scratch/cluster/ccor/feature-learning/',
req_rew = False,
record_runs = False,
):
if n_samples:
n_samples = map(int, n_samples.split(','))
beta_ratio = beta/gam
# append reward to basis when using perfect info?
if training_methods is None:
training_methods = [
#(['covariance', 'prediction', 'value_prediction', 'bellman'],[['theta-all'],['theta-all'],['theta-all'],['theta-all','w']]),
#(['prediction', 'value_prediction', 'bellman'],[['theta-all'],['theta-all'],['theta-all','w']]),
#(['value_prediction'],[['theta-all']]),
#(['value_prediction', 'bellman'],[['theta-all'],['theta-all','w']]),
#(['prediction'],[['theta-all']]),
#(['prediction', 'bellman'], [['theta-all'],['theta-all','w']]),
#(['covariance'], [['theta-all']]),
(['prediction', 'bellman'], [['theta-all'],['theta-all','w']]),
(['bellman', 'prediction', 'bellman'], [['theta-all','w'], ['theta-all'],['theta-all','w']]),
(['full_covariance', 'bellman'], [['theta-all'],['theta-all','w']]),
(['covariance', 'bellman'], [['theta-all'],['theta-all','w']]),
(['full_laplacian'], [['theta-all', 'w']]),
(['laplacian'], [['theta-all', 'w']]),
(['bellman'], [['theta-all', 'w']]), # baseline
]
losses = ['sample-reward', 'test-lsbellman', 'test-bellman', 'test-reward', 'test-model', 'test-fullmodel', # test-training
'true-policy', 'true-policy-uniform', 'true-bellman', 'true-lsbellman', 'true-reward', 'true-model', 'true-fullmodel', 'true-lsq'] \
if n_samples else \
['sample-reward', 'true-policy-uniform', 'true-policy', 'true-bellman', 'true-lsbellman', 'true-reward', 'true-model', 'true-fullmodel', 'true-lsq']
logger.info('building environment of size %i' % env_size)
mdp = grid_world.MDP(walls_on = True, size = env_size)
env = mdp.env
n_states = env_size**2
m = Model(mdp.R, mdp.P, gam = gam)
# create raw data encoder (constant appended in encoder by default)
if encoding is 'tabular':
encoder = TabularFeatures(env_size, append_const = True)
elif encoding is 'tile':
encoder = TileFeatures(env_size, append_const = True)
elif encoding is 'factored':
raise NotImplementedError
def sample(n):
logger.info('sampling from a grid world')
# currently defaults to on-policy sampling
n_extra = calc_discount_horizon(lam, gam, eps) - 1 # mdp returns n+1 states and n rewards
kw = dict(n_samples = n + n_extra, encoder = encoder, req_rew = req_rew)
R, X, _ = mdp.sample_encoding(**kw)
if req_rew:
logger.info('reward required')
assert sum(R.todense()) > 0
logger.info('reward sum: %.2f' % sum(R.todense()))
R_val, X_val, _ = mdp.sample_encoding(**kw)
R_test, X_test, _ = mdp.sample_encoding(**kw)
#losses = ['test-bellman', 'test-reward', 'test-model',
#'true-bellman', 'true-reward', 'true-model', 'true-lsq'] # test-training
weighting = 'policy'
return (X, X_val, X_test), (R, R_val, R_test), weighting
def full_info():
logger.info('using perfect information')
# gen stacked matrices of I, P, P^2, ...
R = numpy.array([])
S = sp.eye(n_states, n_states)
P = sp.eye(n_states, n_states)
for i in xrange(calc_discount_horizon(lam, gam, eps)): # decay epsilon
R = numpy.append(R, P * m.R)
P = m.P * P
S = sp.vstack((S, P))
X = encoder.encode(S)
R = sp.csr_matrix(R[:,None])
X_val = X_test = X
R_val = R_test = R
#losses = ['true-bellman', 'true-reward', 'true-model']
weighting = 'uniform'
return (X, X_val, X_test), (R, R_val, R_test), weighting
reg = None
if l1theta is not None:
reg = ('l1theta', l1theta)
if l1code is not None:
reg = ('l1code', l1code)
if l2code is not None:
reg = ('l2code', l2code)
run_param_keys = ['k','method','encoding','samples', 'reward_samples', 'reward_runs', 'size','weighting',
'lambda','gamma','alpha', 'eta', 'regularization','nonlinear']
def yield_jobs():
for i,n in enumerate(n_samples or [n_states]):
logger.info('creating job with %i samples/states' % n)
# build bellman operator matrices
logger.info('making mixing matrices')
Mphi, Mrew = BellmanBasis.get_mixing_matrices(n, lam, gam,
sampled = bool(n_samples), eps = eps)
for r in xrange(n_runs):
n_features = encoder.n_features
# initialize parameters
theta_init = numpy.random.standard_normal((n_features, k))
theta_init /= numpy.sqrt((theta_init * theta_init).sum(axis=0))
w_init = numpy.random.standard_normal((k+1,1))
w_init = w_init / numpy.linalg.norm(w_init)
# sample or gather full info data
X_data, R_data, weighting = sample(n) if n_samples else full_info()
bb_params = [n_features, [k], beta_ratio]
bb_dict = dict( alpha = alpha, reg_tuple = reg, nonlin = nonlin,
nonzero = nonzero, thetas = [theta_init])
for j, tm in enumerate(training_methods):
loss_list, wrt_list = tm
assert len(loss_list) == len(wrt_list)
run_param_values = [k, tm, encoder, n,
n_reward_samples, n_reward_runs,
env_size, weighting,
lam, gam, alpha, eta,
reg[0]+str(reg[1]) if reg else 'None',
nonlin if nonlin else 'None']
d_run_params = dict(izip(run_param_keys, run_param_values))
yield (train_basis,[d_run_params, bb_params, bb_dict,
env, m, losses, # environment, model and loss list
X_data, R_data, Mphi, Mrew, # training data
max_iter, patience, min_imp, min_delta, # optimization params
fldir, record_runs]) # recording params
# create output file path
date_str = time.strftime('%y%m%d.%X').replace(':','')
out_dir = fldir + 'sirf/output/csv/'
root = '%s.%s_results' % (
date_str,
'n_samples' if n_samples else 'full_info')
d_experiment_params = dict(izip(['k','encoding','size',
'lambda','gamma','alpha','regularization','nl'],
[k, encoder, env_size, lam, gam, alpha,
reg[0]+str(reg[1]) if reg else 'None',
nonlin if nonlin else 'None']))
save_path = out_string(out_dir, root, d_experiment_params, '.csv.gz')
logger.info('saving results to %s' % save_path)
# get column title list ordered params | losses using dummy dicts
d_param = dict(izip(run_param_keys, numpy.zeros(len(run_param_keys))))
d_loss = dict(izip(losses, numpy.zeros(len(run_param_keys))))
col_keys_array,_ = reorder_columns(d_param, d_loss)
with openz(save_path, "wb") as out_file:
writer = csv.writer(out_file)
writer.writerow(col_keys_array)
for (_, out) in condor.do(yield_jobs(), workers):
keys, vals = out
assert (keys == col_keys_array).all() # todo catch
writer.writerow(vals)
def train_basis(d_run_params, basis_params, basis_dict,
env, model, losses,
S_data, R_data, Mphi, Mrew,
max_iter, patience, min_imp, min_delta,
fl_dir, record_runs):
method, weighting, encoder, n_reward_samples, n_reward_runs, env_size = map(lambda x: d_run_params[x],
'method weighting encoding reward_samples reward_runs size'.split())
logger.info('training basis using training method: %s' % str(method))
S, S_val, S_test = S_data
R, R_val, R_test = R_data
n_rows = float(S.shape[0])
# initialize loss dictionary
d_loss_learning = {}
for key in losses:
d_loss_learning[key] = numpy.array([])
loss_list, wrt_list = method
assert len(loss_list) == len(wrt_list)
logger.info('constructing bellman basis')
basis = BellmanBasis(*basis_params, **basis_dict)
def record_loss(d_loss):
# TODO automate/shorten this
# record losses with test set
for loss, arr in d_loss.items():
if loss == 'sample-reward':
val = sample_policy_reward(env, basis.estimated_value(encoder.B, False), n_reward_samples, n_reward_runs)
elif loss == 'test-training':
val = basis.loss(basis.flat_params, S_test, R_test, Mphi, Mrew) / n_rows
elif loss == 'test-bellman':
val = basis.loss_be(*(basis.params + [S_test, R_test, Mphi, Mrew])) / n_rows
elif loss == 'test-lsbellman':
val = basis.loss_lsbe(*(basis.params + [S_test, R_test, Mphi, Mrew])) / n_rows
elif loss == 'test-reward':
val = basis.loss_r(*(basis.params + [S_test, R_test, Mphi, Mrew])) / n_rows
elif loss == 'test-model':
val = basis.loss_m(*(basis.params + [S_test, R_test, Mphi, Mrew])) / n_rows
elif loss == 'test-fullmodel':
val = basis.loss_fm(*(basis.params + [S_test, R_test, Mphi, Mrew])) / n_rows
elif loss == 'true-policy':
val = model.policy_distance(Bs, weighting = 'policy')
elif loss == 'true-policy-uniform':
val = model.policy_distance(Bs, weighting = 'uniform')
elif loss == 'true-bellman':
val = model.bellman_error(Bs, w = basis.params[-1], weighting = weighting)
elif loss == 'true-lsbellman':
val = model.bellman_error(Bs, weighting = weighting)
elif loss == 'true-reward':
val = model.reward_error(Bs, weighting = weighting)
elif loss == 'true-model':
val = model.model_error(Bs, weighting = weighting)
elif loss == 'true-fullmodel':
val = model.fullmodel_error(Bs, weighting = weighting)
elif loss == 'true-lsq':
val = model.lsq_error(Bs, weighting = weighting)
else: print loss; assert False
d_loss[loss] = numpy.append(arr, val)
return d_loss
switch = [] # list of indices where a training method switch occurred
it = 0
# train once on w to initialize
basis.set_loss('bellman', ['w'])
basis.set_params(scipy.optimize.fmin_cg(
basis.loss, basis.flat_params, basis.grad,
args = (S, R, Mphi, Mrew),
full_output = False,
maxiter = max_iter,
))
# TODO keep?
IM = encoder.weights_to_basis(basis.thetas[-1])
Bs = basis.encode(encoder.B, False)
if len(basis.thetas) == 1:
assert (IM == Bs).all()
d_loss_learning = record_loss(d_loss_learning)
for loss, wrt in zip(loss_list, wrt_list):
waiting = 0
best_params = None
best_test_loss = 1e20
if 'w' in wrt_list: # initialize w to the lstd soln given the current basis
logger.info('initializing w to lstd soln')
basis.params[-1] = BellmanBasis.lstd_weights(basis.encode(S), R, Mphi, Mrew) # TODO change to iteration of opt on w?
try:
while (waiting < patience):
it += 1
logger.info('*** iteration ' + str(it) + '***')
old_params = copy.deepcopy(basis.flat_params)
for loss_, wrt_ in ((loss, wrt), ('bellman', ['w'])):
basis.set_loss(loss_, wrt_)
basis.set_params(scipy.optimize.fmin_cg(
basis.loss, basis.flat_params, basis.grad,
args = (S, R, Mphi, Mrew),
full_output = False,
maxiter = max_iter,
))
basis.set_loss(loss, wrt) # reset loss back from bellman
delta = numpy.linalg.norm(old_params-basis.flat_params)
logger.info('delta theta: %.2f' % delta)
norms = numpy.apply_along_axis(numpy.linalg.norm, 0, basis.thetas[0])
logger.info( 'column norms: %.2f min / %.2f avg / %.2f max' % (
norms.min(), norms.mean(), norms.max()))
err = basis.loss(basis.flat_params, S_val, R_val, Mphi, Mrew)
if err < best_test_loss:
if ((best_test_loss - err) / best_test_loss > min_imp) and (delta > min_delta):
waiting = 0
else:
waiting += 1
logger.info('iters without better %s loss: %i' % (basis.loss_type, int(waiting)))
best_test_loss = err
best_params = copy.deepcopy(basis.flat_params)
logger.info('new best %s loss: %.2f' % (basis.loss_type, best_test_loss))
else:
waiting += 1
logger.info('iters without better %s loss: %i' % (basis.loss_type, int(waiting)))
Bs = basis.encode(encoder.B, False)
d_loss_learning = record_loss(d_loss_learning)
except KeyboardInterrupt:
logger.info( '\n user stopped current training loop')
# set params to best params from last loss
basis.set_params(vec = best_params)
switch.append(it-1)
sparse_eps = 1e-5
Bs = basis.encode(encoder.B, False)
d_loss_learning = record_loss(d_loss_learning)
logger.info( 'final test bellman error: %.2f' % model.bellman_error(Bs, weighting = weighting))
logger.info( 'final sparsity: ' + str( [(numpy.sum(abs(th) < sparse_eps) / float(len(th.flatten()))) for th in basis.params]))
# edit d_run_params to not include wrt list in method
d_run_params['method'] = '-'.join(d_run_params['method'][0])
# TODO change to output log file and plot afterwards
if record_runs:
# save results!
# plot basis functions
plot_stacked_features(Bs[:, :36])
figst = out_string(fl_dir+'sirf/output/plots/learning/', 'basis_stacked', d_run_params, '.pdf')
plt.savefig(figst)
# plot the basis functions again!
plot_features(Bs)
figst = out_string(fl_dir+'sirf/output/plots/learning/', 'basis_all', d_run_params, '.pdf')
plt.savefig(figst)
# plot learning curves
pltd = plot_learning_curves(d_loss_learning, switch, filt = 'test')
if pltd: # if we actually plotted
plt.savefig(out_string(fl_dir+'sirf/output/plots/learning/', 'test_loss', d_run_params, '.pdf') )
pltd = plot_learning_curves(d_loss_learning, switch, filt = 'true')
if pltd:
plt.savefig(out_string(fl_dir+'sirf/output/plots/learning/', 'true_loss', d_run_params, '.pdf'))
# plot value functions
plot_value_functions(env_size, model, Bs)
plt.savefig(out_string(fl_dir+'sirf/output/plots/learning/', 'value_funcs', d_run_params, '.pdf'))
# plot spectrum of reward and features
gen_spectrum(Bs, model.P, model.R)
plt.savefig(out_string(fl_dir+'sirf/output/plots/learning/', 'spectrum', d_run_params, '.pdf'))
d_loss_batch = dict(izip(d_loss_learning.keys(), map(lambda x: x[-1],
d_loss_learning.values())))
# returns keys_array, values_array
return reorder_columns(d_run_params, d_loss_batch)
