E-resources
Peer reviewed
-
Kim, Joohyun; Hong, Seohee; Hong, Sengphil; Kim, Jaehoon
Concurrency and computation, 10 August 2021, Volume: 33, Issue: 15Journal Article
Summary Reinforcement learning (RL) is utilized in a wide range of real‐world applications. Typical applications include single agent‐based RL. However, most practical tasks require multiple agents for cooperative control processes. Multiple‐agent RL demands complicated design, and numerous design possibilities should be considered for its practical usefulness. We propose two RL implementations for a message‐queuing telemetry transport (MQTT) protocol system. Two types of implementations improve the communication efficiency of MQTT: (i) single‐broker‐agent implementation and (ii) multiple‐publisher‐agents implementation. We focused on different message priorities in a dynamic environment for each implementation. The proposed implementations improve communication efficiency by adjusting the loop cycle time of the broker or by learning the message importance. The proposed MQTT control scheme improves the battery efficiency of Internet‐of‐Things (IoT)‐based devices with relatively insufficient battery power.
Shelf entry
Permalink
- URL:
Impact factor
Access to the JCR database is permitted only to users from Slovenia. Your current IP address is not on the list of IP addresses with access permission, and authentication with the relevant AAI accout is required.
Year | Impact factor | Edition | Category | Classification | ||||
---|---|---|---|---|---|---|---|---|
JCR | SNIP | JCR | SNIP | JCR | SNIP | JCR | SNIP |
Select the library membership card:
If the library membership card is not in the list,
add a new one.
DRS, in which the journal is indexed
Database name | Field | Year |
---|
Links to authors' personal bibliographies | Links to information on researchers in the SICRIS system |
---|
Source: Personal bibliographies
and: SICRIS
The material is available in full text. If you wish to order the material anyway, click the Continue button.