A novel reinfrocement learning method was developed where two communicating systems could learn to predistort their signals to compensate for distortion in the channel. The two predistorters co-adapt using the output of the other predistorter to determine their own reinforcement signal. This approach makes it possible to adapt to changes in the channel characteristics on-line, as opposed to off-line learning of the current systems.