  Recent advancements in model-free deep reinforcement learning have enabled
efficient agent training. However, challenges arise when determining the region
of attraction for these controllers, especially if the region does not fully
cover the desired area. This paper addresses this issue by introducing a
feedback motion control algorithm that utilizes data-driven techniques and
neural networks. The algorithm constructs a graph of connected
reinforcement-learning based controllers, each with its own defined region of
attraction. This incremental approach effectively covers a bounded region of
interest, creating a trajectory of interconnected nodes that guide the system
from an initial state to the goal. Two approaches are presented for connecting
nodes within the algorithm. The first is a tree-structured method, facilitating
"point-to-point" control by constructing a tree connecting the initial state to
the goal state. The second is a graph-structured method, enabling
"space-to-space" control by building a graph within a bounded region. This
approach allows for control from arbitrary initial and goal states. The
proposed method's performance is evaluated on a first-order dynamic system,
considering scenarios both with and without obstacles. The results demonstrate
the effectiveness of the proposed algorithm in achieving the desired control
objectives.
