Attention-based deep reinforcement learning for autonomous agents

A computer-implemented method, comprising:

• deploying, in response to a first programmatic request received at a network-accessible service of a cloud computing environment from a client, a machine learning model to an autonomous agent, wherein the machine learning model is trained to initiate actions of the autonomous agent based at least in part on analysis of an environment of the autonomous agent;
• causing to be presented, in response to a second programmatic request received at the network-accessible service from the client, an indication of a first importance assigned to a first portion of an environment of the autonomous agent by the machine learning model, wherein the first importance exceeds a second importance assigned to a second portion of the environment;
• and
• causing, by the machine learning model, the autonomous agent to initiate a particular action, wherein the particular action is selected based at least in part on contents of the first portion of the environment.

A system, comprising:

• one or more computing devices;
• wherein the one or more computing devices include instructions that upon execution on or across one or more processors cause the one or more processors to:
• deploy, in response to a first programmatic request received at a network-accessible service of a cloud computing environment from a client, a machine learning model to an autonomous agent, wherein the machine learning model is trained to initiate actions of the autonomous agent based at least in part on analysis of an environment of the autonomous agent;
• cause to be presented, in response to a second programmatic request received at the network-accessible service from the client, an indication of a first importance assigned to a first portion of an environment of the autonomous agent by the machine learning model, wherein the first importance exceeds a second importance assigned to a second portion of the environment;
• and
• cause, by the machine learning model, the autonomous agent to initiate a particular action, wherein the particular action is selected based at least in part on contents of the first portion of the environment.

One or more non-transitory computer-accessible storage media storing program instructions that when executed on or across one or more processors:

• deploy, in response to a first programmatic request received at a network-accessible service of a cloud computing environment from a client, a machine learning model to an autonomous agent, wherein the machine learning model is trained to initiate actions of the autonomous agent based at least in part on analysis of an environment of the autonomous agent;
• cause to be presented, in response to a second programmatic request received at the network-accessible service from the client, an indication of a first importance assigned to a first portion of an environment of the autonomous agent by the machine learning model, wherein the first importance exceeds a second importance assigned to a second portion of the environment;
• and
• cause, by the machine learning model, the autonomous agent to initiate a particular action, wherein the particular action is selected based at least in part on contents of the first portion of the environment.