def Adam(cost, params, alpha=floatX(0.001), beta_1=floatX(0.9), beta_2=floatX(0.999), epsilon=floatX(1e-8)):
'''
Follows the psuedo-code from
ADAM: A METHOD FOR STOCHASTIC OPTIMIZATION
http://arxiv.org/pdf/1412.6980v8.pdf
'''
updates = []
t = theano.shared(value=floatX(1.), name='t')
grads = T.grad(cost, params)
alpha_t = alpha * T.sqrt(floatX(1.) - beta_2**t) / (floatX(1.) - beta_1**t)
for param, gparam in zip(params, grads):
value = param.get_value(borrow=True)
# initialize first and second moment updates parameter-wise
m = theano.shared(value=np.zeros(value.shape, dtype=value.dtype),
broadcastable=param.broadcastable, name='m')
v = theano.shared(value=np.zeros(value.shape, dtype=value.dtype),
broadcastable=param.broadcastable, name='v')
# update biased first/second moment estimates
m_t = beta_1 * m + (floatX(1.) - beta_1) * gparam
v_t = beta_2 * v + (floatX(1.) - beta_2) * T.sqr(gparam)
# use the efficient update from sec. 2 of the paper to avoid
# computing the unbiased estimates
g_t = m_t / (T.sqrt(v_t) + epsilon)
param_t = param - alpha_t * g_t
# store changes to the shared variables
updates.append((m, m_t))
updates.append((v, v_t))
updates.append((param, param_t))
updates.append((t, t + 1))
return updates
def __init__(self, dqn_mt, gamma=0.95, l2_reg=0.0, lr=1e-3,
memory_size=250, minibatch_size=64,
nn_num_batch=1, nn_num_iter=2, regularizer={},
update_freq=1, target_freq=10, skip_frame=0,
frames_per_action=4,
exploration_kwargs={
'method': 'eps-greedy',
'epsilon': 0.1
}):
'''
(TODO): task should be task info.
we don't use all of task properties/methods here.
only gamma and state dimension.
and we allow task switching.
'''
self.dqn = dqn_mt
self.dqn_frozen = dqn_mt.copy()
self.l2_reg = floatX(l2_reg)
self.lr = floatX(lr)
self.target_freq = target_freq
self.update_freq = update_freq
self.memory_size = memory_size
self.minibatch_size = minibatch_size
self.gamma = floatX(gamma)
self.regularizer = regularizer
self.skip_frame = skip_frame
self.exploration_kwargs = exploration_kwargs
self.frames_per_action = frames_per_action
# for now, keep experience as a list of tuples
self.experience = []
self.exp_idx = 0
self.total_exp = 0
# used for streaming updates
self.last_state = None
self.last_valid_actions = None
self.last_action = None
# params for nn optimization.
self.nn_num_batch = nn_num_batch
self.nn_num_iter = nn_num_iter
# dianostics.
self.diagnostics = {
'nn-error': [] # training of neural network on mini-batches.
}
# compile back-propagtion network
self._compile_bp()
def _get_frame(self):
if self.state_type == 'pixel':
from scipy.misc import imresize
img = self.curr_screen_rgb
img = rgb2yuv(img)[:, :, 0] # get Y channel, according to Nature paper.
img = imresize(img, (84, 84), interp='bicubic')
return img / floatX(255.0)
elif self.state_type == 'ram':
return self._get_ram_state()
elif self.state_type == '1hot':
return self._get_1hot_state()
else:
raise NotImplementedError()
def curr_state(self):
'''
return raw pixels.
'''
return np.array(self.frames, dtype=floatX) / floatX(255.) # normalize
