Automated reinforcement-learning-based application manager that uses action tags and metric tags

A method that improves the computational efficiency of an automated reinforcement-learning-based application manager having one or more processors, one or more memories, one or more communications subsystems, a set of actions that can be issued by an iterative control process to a computing environment, controlled by the automated reinforcement-learning-based application manager, having a set of metrics, the method comprising:

• associating tags with actions and metrics;
• and
• enhancing the iterative control process to repeatedly
• select and issue a next action, according to a control policy that uses the current computational-environment state and that accesses values of tags associated with actions and metrics, to control the computing environment, and
• receives a reward and one of an observation and a state from the computing environment, in response to execution of the issued next action, which the control process uses to attempt to learn an optimal or near-optimal control policy, over time.

A physical data-storage device encoded with computer instructions that, when executed by one or more processors of a computer system that implements an automated reinforcement-learning-based application manager having one or more processors, one or more memories, one or more communications subsystems, a set of actions that can be issued by an iterative control process to a computing environment, controlled by the automated reinforcement-learning-based application manager, having a set of metrics, controls the automated reinforcement-learning-based application manager to

• associate tags with actions and metrics;
• and
• repeatedly, by the iterative control process,
• select and issue a next action, according to a control policy that uses the current computational-environment state and that accesses values of tags associated with actions and metrics, to control the computing environment, and
• receives a reward and one of an observation and a state from the computing environment, in response to execution of the issued next action, which the control process uses to attempt to learn an optimal or near-optimal control policy, over time.