A processing unit can determine a first feature value corresponding to a session by operating a first network computational model (NCM) based part on information of the session. The processing unit can determine respective second feature values corresponding to individual actions of a plurality of actions by operating a second NCM. The second NCM can use a common set of parameters in determining the second feature values. The processing unit can determine respective expectation values of some of the actions of the plurality of actions based on the first feature value and the respective second feature values. The processing unit can select a first action of the plurality of actions based on at least one of the expectation values. In some examples, the processing unit can operate an NCM to determine expectation values based on information of a session and information of respective actions.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A system, comprising: a communications interface; one or more processing unit(s) adapted to execute modules; and one or more computer-readable media having thereon a plurality of modules, the plurality of modules comprising: a module of a representation engine that is configured to: operate a first network computational model (NCM) based at least in part on information of a session to provide a first feature value corresponding to the session; and operate a second NCM to provide respective second feature values corresponding to individual actions of a plurality of actions; a module of an action engine that is configured to: determine respective expectation values of at least some of the actions of the plurality of actions based at least in part on the first feature value and the respective second feature values; and select a first action of the plurality of actions based at least in part on at least one of the expectation values; a module of a training engine that is configured to: train the first NCM based at least in part on the first action and a result value to provide a third NCM; train the second NCM based at least in part on the first action and the result value to provide a fourth NCM; and a module of a communications engine that is configured to: provide an indication of the first action via the communications interface; determine result information; and determine the result value based at least in part on the result information. 2. A system as recited in claim 1, wherein the communications engine is configured to receive the result information via the communications interface. 3. A system as recited in claim 1, wherein the communications engine is further configured to determine the result information based at least in part on determining that a selected timeout period has elapsed without receiving a response to the indication of the first action. 4. A system as recited in claim 1, wherein the communications engine is further configured to: receive a first data item and a second data item via the communications interface, the first data item and the second data items associated with the session; and determine the result information by determining a duration between a timestamp of receipt of the first data item and a timestamp of receipt of the second data item. 5. A system as recited in claim 1, wherein the first NCM and the second NCM comprise respective neural networks and the training engine is configured to train the first NCM and the second NCM using a reinforcement-learning update rule. 6. A system as recited in claim 1, wherein: the first feature value and the second feature value corresponding to a first action of the plurality of actions comprise respective vectors; and the action engine is configured to determine the expectation value corresponding to the first action as an inner product of the first feature value and the second feature value corresponding to the first action. 7. A system as recited in claim 1, wherein the training engine is configured to: train the first NCM further based on at least one of the second feature values; and train the second NCM further based on at least the first feature value. 8. A system as recited in claim 1, wherein the representation engine is further configured to: receive data of the session; and determine at least part of the information of the session based at least in part on the data. 9. A method, comprising: determining a first feature value corresponding to a session by operating a first network computational model (NCM) based at least in part on information of the session; determining respective second feature values corresponding to individual actions of a plurality of actions by operating a second NCM, wherein the second NCM uses a common set of parameters in determining the second feature values; determining respective expectation values associated with at least some of the actions of the plurality of actions based at least in part on the first feature value and the respective second feature values; and selecting a first action of the plurality of actions based at least in part on at least one of the expectation values. 10. A method as recited in claim 9, further comprising: receiving textual data of at least one action of the plurality of actions; determining a representation of the textual data; and determining the second feature value corresponding to the at least one action based at least in part on the representation. 11. A method as recited in claim 9, further comprising: receiving at least one data item via a network interface; determining the information of the session based at least in part on the at least one data item; and transmitting an indication of the first action via the network interface. 12. A method as recited in claim 9, further comprising: training the first NCM based at least in part on the first action and a result value to provide a third NCM; and training the second NCM based at least in part on the first action and the result value to provide a fourth NCM. 13. A method as recited in claim 12, wherein the first NCM and the second NCM comprise respective neural networks and the training engine is configured to train the first NCM and the second NCM using a reinforcement-learning update rule. 14. A method as recited in claim 12, further comprising receiving the result value via a network interface. 15. A method as recited in claim 12, further comprising determining the result value based at least in part on respective timestamps of a plurality of data items received via a network interface. 16. A method as recited in claim 12, further comprising: determining a first revised feature value corresponding to the session by operating the third NCM based at least in part on revised information of the session; determining respective second revised feature values corresponding to at least two of the actions of the plurality of actions by operating the fourth NCM, wherein the training the second NCM includes determining a common revised set of parameters and the fourth NCM uses the common revised set of parameters in determining the second feature values; determining respective revised expectation values associated with the at least two of the actions based at least in part on the first revised feature value and the respective second revised feature values; and selecting a second action of the plurality of actions based at least in part on at least one of the revised expectation values. 17. An apparatus, comprising: at least one processor; and a computer-readable medium including instructions to, when executed by the at least one processor, cause the at least one processor to: operate a network computational model (NCM) to separately determine respective expectation values for actions of a plurality of actions, individual expectation values determined based at least in part on: information of a state of a session; common parameters of the NCM; and information of the respective action of the plurality of actions; and select a first action of the plurality of actions based at least in part on at least one of the expectation values. 18. An apparatus as recited in claim 17, the instructions further to cause the at least one processor to: train the network computational model based at least in part on the first action and a result value to provide a second NCM; operate the second NCM to separately determine respective revised expectation values for the actions of the plurality of actions, the individual revised expectation values determined based at least in part on: information of a revised state of a session; and common parameters of the second NCM; and select a second action of the plurality of actions based at least in part on at least one of the revised expectation values. 19. An apparatus as recited in claim 17, the instructions further to cause the at least one processor to: operate a first computational model to determine a feature value of the session based at least in part on the information of the state of the session; operate a second computational model to determine a feature value of the first action of the plurality of actions; and operate the NCM having the feature value of the session and the feature value of the first action as input to determine the expectation value of the first action. 20. An apparatus as recited in claim 17, the instructions further to cause the at least one processor to: receive data of at least part of the state of the session; and operate a computational model to determine at least part of the information of the state of the session based at least in part on the data.