def __init__(self, session, ob_dim=None, n_epochs=20, stepsize=1e-3):
""" The network gets constructed upon initialization so future calls to
self.fit will remember this.
Right now we assume a preprocessing which results ob_dim*2+1 dimensions,
and we assume a fixed neural network architecture (input-50-50-1, fully
connected with tanh nonlineariites), which we should probably change.
The number of outputs is one, so that ypreds_n is the predicted vector
of state values, to be compared against ytargs_n. Since ytargs_n is of
shape (n,), we need to apply a "squeeze" on the final predictions, which
would otherwise be of shape (n,1). Bleh.
"""
# Value function V(s_t) (or b(s_t)), parameterized as a neural network.
self.ob_no = tf.placeholder(shape=[None, ob_dim*2+1], name="nnvf_ob", dtype=tf.float32)
self.h1 = layers.fully_connected(self.ob_no,
num_outputs=50,
weights_initializer=layers.xavier_initializer(uniform=True),
activation_fn=tf.nn.tanh)
self.h2 = layers.fully_connected(self.h1,
num_outputs=50,
weights_initializer=layers.xavier_initializer(uniform=True),
activation_fn=tf.nn.tanh)
self.ypreds_n = layers.fully_connected(self.h2,
num_outputs=1,
weights_initializer=layers.xavier_initializer(uniform=True),
activation_fn=None)
self.ypreds_n = tf.reshape(self.ypreds_n, [-1]) # (?,1) --> (?,). =)
# Form the loss function, which is the simple (mean) L2 error.
self.n_epochs = n_epochs
self.lrate = stepsize
self.ytargs_n = tf.placeholder(shape=[None], name="nnvf_y", dtype=tf.float32)
self.l2_error = tf.reduce_mean(tf.square(self.ypreds_n - self.ytargs_n))
self.fit_op = tf.train.AdamOptimizer(self.lrate).minimize(self.l2_error)
self.sess = session
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