  Underwater robots in shallow waters usually suffer from strong wave forces,
which may frequently exceed robot's control constraints. Learning-based
controllers are suitable for disturbance rejection control, but the excessive
disturbances heavily affect the state transition in Markov Decision Process
(MDP) or Partially Observable Markov Decision Process (POMDP). Also, pure
learning procedures on targeted system may encounter damaging exploratory
actions or unpredictable system variations, and training exclusively on a prior
model usually cannot address model mismatch from the targeted system. In this
paper, we propose a transfer learning framework that adapts a control policy
for excessive disturbance rejection of an underwater robot under dynamics model
mismatch. A modular network of learning policies is applied, composed of a
Generalized Control Policy (GCP) and an Online Disturbance Identification Model
(ODI). GCP is first trained over a wide array of disturbance waveforms. ODI
then learns to use past states and actions of the system to predict the
disturbance waveforms which are provided as input to GCP (along with the system
state). A transfer reinforcement learning algorithm using Transition Mismatch
Compensation (TMC) is developed based on the modular architecture, that learns
an additional compensatory policy through minimizing mismatch of transitions
predicted by the two dynamics models of the source and target tasks. We
demonstrated on a pose regulation task in simulation that TMC is able to
successfully reject the disturbances and stabilize the robot under an empirical
model of the robot system, meanwhile improve sample efficiency.
