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Huber Loss in QR-DQN #2

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Huber Loss in QR-DQN #2

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15 changes: 13 additions & 2 deletions QR-DQN/QR-DQN.py
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Original file line number Diff line number Diff line change
Expand Up @@ -47,8 +47,8 @@ def __init__(self, state_dim, action_dim):
self.action_dim = action_dim
self.atoms = args.atoms
self.tau = [(2*(i-1)+1)/(2*self.atoms) for i in range(1, self.atoms+1)]
self.huber_loss = tf.keras.losses.Huber(
reduction=tf.keras.losses.Reduction.NONE)
# self.huber_loss = tf.keras.losses.Huber(
# reduction=tf.keras.losses.Reduction.NONE)
self.opt = tf.keras.optimizers.Adam(args.lr)
self.model = self.create_model()

Expand All @@ -61,6 +61,17 @@ def create_model(self):
Reshape([self.action_dim, self.atoms])
])


def huber_loss(self, y_true, y_pred, clip_delta=1.0):
error = y_true - y_pred
cond = tf.keras.backend.abs(error) < clip_delta

squared_loss = 0.5 * tf.keras.backend.square(error)
linear_loss = clip_delta * (tf.keras.backend.abs(error) - 0.5 * clip_delta)

return tf.where(cond, squared_loss, linear_loss)


def quantile_huber_loss(self, target, pred, actions):
pred = tf.reduce_sum(pred * tf.expand_dims(actions, -1), axis=1)
pred_tile = tf.tile(tf.expand_dims(pred, axis=2), [1, 1, self.atoms])
Expand Down