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An op for generating noise from a zero-mean Ornstein-Uhlenbeck process.
tf_agents.utils.common.ornstein_uhlenbeck_process( initial_value, damping=0.15, stddev=0.2, seed=None, scope='ornstein_uhlenbeck_noise' )
The Ornstein-Uhlenbeck process is a process that generates temporally correlated noise via a random walk with damping. This process describes the velocity of a particle undergoing brownian motion in the presence of friction. This can be useful for exploration in continuous action environments with momentum.
The temporal update equation is:
x_next = (1 - damping) * x + N(0, std_dev)
initial_value: Initial value of the process.
damping: The rate at which the noise trajectory is damped towards the mean. We must have 0 <= damping <= 1, where a value of 0 gives an undamped random walk and a value of 1 gives uncorrelated Gaussian noise. Hence in most applications a small non-zero value is appropriate.
stddev: Standard deviation of the Gaussian component.
seed: Seed for random number generation.
scope: Scope of the variables.
An op that generates noise.