Specialized Deep Residual Policy Safe Reinforcement Learning-Based
  Controller for Complex and Continuous State-Action Spaces
Ammar N. Abbas Georgios C. Chasparis John D. Kelleher
Traditional controllers have limitations as they rely on prior knowledge
about the physics of the problem, require modeling of dynamics, and struggle to
adapt to abnormal situations. Deep reinforcement learning has the potential to
address these problems by learning optimal control policies through exploration
in an environment. For safety-critical environments, it is impractical to
explore randomly, and replacing conventional controllers with black-box models
is also undesirable. Also, it is expensive in continuous state and action
spaces, unless the search space is constrained. To address these challenges we
propose a specialized deep residual policy safe reinforcement learning with a
cycle of learning approach adapted for complex and continuous state-action
spaces. Residual policy learning allows learning a hybrid control architecture
where the reinforcement learning agent acts in synchronous collaboration with
the conventional controller. The cycle of learning initiates the policy through
the expert trajectory and guides the exploration around it. Further, the
specialization through the input-output hidden Markov model helps to optimize
policy that lies within the region of interest (such as abnormality), where the
reinforcement learning agent is required and is activated. The proposed
solution is validated on the Tennessee Eastman process control.