An existing machine-learning method called recurrent reinforcement learning (RRL) was chosen as the underlying algorithm for ARL.
Here, we argue that behavioral ecology will benefit from drawing more than it currently does on a complementary collection of tools, called reinforcement learning methods.
Such decision making strategies are generally called reinforcement learning.
In this paper, we introduce a model-based Averagereward Reinforcement Learning method called H-learning and show that it converges more quickly and robustly than its discounted counterpart in the domain of scheduling a simulated Automatic Guided Vehicle (AGV).
In this paper, an on-line reinforcement learning (RL) based method, called goal representation adaptive dynamic programming (GrADP), is employed to adaptive control of units in an island smart grid.
Recently, a new method called OPTICA has been introduced, which through local reinforcement with inserts improves wear resistance of ductile iron without compromising other properties.
This is called negative reinforcement — reinforcement that occurs when a behavior prevents a stimulus from occurring.
This method is called the translation method.
This method is called the avalanche method.
This method is called the garden method.
