Apparatus and method for distributed reinforcement learning

An apparatus for distributed reinforcement learning, the apparatus comprising:

• a global experience memory configured to receive and store at least one piece of proxy state information and at least one proxy action execution probability, the at least one piece of proxy state information and the at least one proxy action execution probability received from a plurality of agents, the at least one piece of proxy state information configured in correspondence to at least one pre-designated state cluster, the at least one proxy action execution probability mapped in correspondence to each of the at least one piece of proxy state information;
• a global computation unit configured to compute a global action execution probability in a pre-designated manner from the at least one proxy action execution probability mapped to each of the at least one piece of proxy state information stored in the global experience memory;
• and
• a global proxy experience memory configured to map and store the computed global action execution probability for redistributing transmission to each of the plurality of agents, the global action execution probability stored mapped correspondingly to the at least one piece of proxy state information.

A method for distributed reinforcement learning, the method comprising:

• receiving state information and estimating an action execution probability from the state information according to a previously learned pattern estimation method, the state information representing a state of a surrounding environment, the action execution probability probabilistically representing an action to be executed;
• learning a pattern estimation method for estimating the action execution probability by determining a loss value from the action execution probability and a global action execution probability transmitted from a central server;
• storing the state information and the action execution probability corresponding to the state information in a mapped form;
• clustering a plurality of pieces of state information in a pre-designated manner to classify the plurality of pieces of state information into at least one state cluster having a piece of proxy state information configured therefor beforehand, and obtaining a proxy action execution probability for each state cluster from action execution probabilities mapped correspondingly to pieces of state information included in the at least one state cluster;
• and
• storing the proxy state information and proxy action execution probability corresponding to each of the at least one state cluster in a mapped form for transmitting to the central server.

A method for distributed reinforcement learning, the method comprising:

• receiving and storing at least one piece of proxy state information configured in correspondence to at least one pre-designated state cluster and at least one proxy action execution probability mapped in correspondence to the at least one piece of proxy state information, the at least one piece of proxy state information and the at least one proxy action execution probability received from a plurality of agents;
• computing a global action execution probability in a pre-designated manner from the stored at least one proxy action execution probability mapped correspondingly to the at least one piece of proxy state information;
• and
• storing the global action execution probability computed for each of the at least one piece of proxy state information in a mapped form for redistributing transmission to each of the plurality of agents.