SYSTEM AND METHOD FOR OPTIMIZING COMMUNICATIONS USING REINFORCEMENT LEARNING
A system and method for automatically optimizing states of communications and operations in a contact center, using a reinforcement learning module comprising a reinforcement learning server and an optimization server introduced to existing infrastructure of the contact center, that, through use of a model set up as a partially observable Markov chain with a Baum-Welch algorithm used to infer parameters and rewards added to form a partially observable Markov decision process, is solved to provide an optimal action policy to use in each state of a contact center, thereby ultimately optimizing states of communications and operations for an overall return.
1 . A system for optimizing interaction routing in a contact center using reinforcement learning, comprising:
a reinforcement learning server comprising at least a plurality of programming instructions stored in a memory and operating on a processor of a network-connected computing device, wherein the plurality of programming instructions, when operating on the processor, cause the processor to:
(a) observe and analyze historical and current data using a retrain and design module;
(b) develop a training set for use in a partially observable Markov chain model;
(c) assign reward values to specific states for use in a partially observable Markov decision process model;
(d) design and train the partially observable Markov decision process model using the retrain and design module to achieve a desired outcome;
(e) form the partially observable Markov decision process model by fitting the partially observable Markov chain model with a Baum-Welch algorithm to infer parameters based on observations;
(f) direct an optimization server to apply the partially observable Markov decision process model;
(g) record results of actions carried out by the optimization server to a learning database;
(h) observe and analyze outcomes of the actions stored in the learning database; and
(i) repeat steps (b) through (h) iteratively; and
an optimization server comprising at least a plurality of programming instructions stored in a memory and operating on a processor of a network-connected computing device, wherein the plurality of programming instructions, when operable on the processor, cause the processor to:
(j) apply actions to states as directed by the reinforcement learning server;
(k) maintain a current version of the partially observable Markov decision process model received from the reinforcement learning server;
(l) direct a routing server to route interactions based on optimal actions determined by the partially observable Markov decision process model; and
(m) analyze events received from the routing server and other contact center components to determine outcomes achieved by the directed interaction routing.
2 . (canceled)