(beta, w_max, thresholds)
return tl.Fn('AWRLoss', f)
Definition of the Advantage Weighted Regression (AWR) loss.
Definition of the Advantage Weighted Regression (AWR) loss.
Definition of the Advantage Weighted Regression ( AWR ) loss .
def AWRLoss(beta, w_max, thresholds):  # pylint: disable=invalid-name
  """Definition of the Advantage Weighted Regression (AWR) loss."""
  def f(log_probs, advantages, old_log_probs, mask):
    del old_log_probs  # Not used in AWR.
    weights = jnp.minimum(awr_weights(advantages, beta, thresholds), w_max)
    return -jnp.sum(log_probs * weights * mask) / jnp.sum(mask)
  return tl.Fn('AWRLoss', f)
def AWRLoss ( beta , w_max , thresholds ) : # pylint: disable=invalid-name def f ( log_probs , advantages , old_log_probs , mask ) : del old_log_probs # Not used in AWR. weights = jnp . minimum ( awr_weights ( advantages , beta , thresholds ) , w_max ) return - jnp . sum ( log_probs * weights * mask ) / jnp . sum ( mask ) return tl . Fn ( 'AWRLoss' , f )