  Performing intervention tasks in the maritime domain is crucial for safety
and operational efficiency. The unpredictable and dynamic marine environment
makes the intervention tasks such as object manipulation extremely challenging.
This study proposes a robust solution for object manipulation from a dock in
the presence of disturbances caused by sea waves. To tackle this challenging
problem, we apply a deep reinforcement learning (DRL) based algorithm called
Soft. Actor-Critic (SAC). SAC employs an actor-critic framework; the actors
learn a policy that minimizes an objective function while the critic evaluates
the learned policy and provides feedback to guide the actor-learning process.
We trained the agent using the PyBullet dynamic simulator and tested it in a
realistic simulation environment called MBZIRC maritime simulator. This
simulator allows the simulation of different wave conditions according to the
World Meteorological Organization (WMO) sea state code. Simulation results
demonstrate a high success rate in retrieving the objects from the dock. The
trained agent achieved an 80 percent success rate when applied in the
simulation environment in the presence of waves characterized by sea state 2,
according to the WMO sea state code
