Multi-Agent Reinforcement Learning Hidden Layer Combination

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Solution Overview

Problem

Multi-agent reinforcement learning systems face complexity and inefficiency due to the non-stationary nature of environments caused by interactions among multiple agents, leading to increased computational and transmission costs as the number of agents grows.

Innovation Solution

The proposed solution involves each agent learning and transmitting only optimized state information, combining local and remote hidden layer outputs using attention networks, multi-head attention networks, MAX, or AVERAGE pooling layers to generate a consolidated, fixed-size combined hidden layer output, reducing information transmission and processing complexity.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Device complexity

If each agent is trained as an independent learner in MARL systems, then individual agent learning is simplified, but the system becomes non-stationary and computational costs increase

Engineering Contradiction:

Improveagent training complexityVSAvoidsystem stationarity

Core Design Contradiction:

Device complexityVSReliability

Solution Approach 1:

The patent segments the global state information into local hidden layer outputs for each agent, where each agent processes only its relevant portion of the environment state through its own neural network hidden layer, rather than processing complete global states independently. This segmentation maintains individual agent simplicity while enabling coordinated learning through shared representations.

Inventive Principle:

Principle #1Segmentation

Solution Approach 2:

The patent merges the local hidden layer outputs from multiple agents into a combined hidden layer representation that captures collective system state information. This combining operation creates a unified system view that restores stationarity by aggregating individual agent perspectives into a coherent global representation.

Inventive Principle:

Principle #5Merging (Combining)

2Adaptability or versatility

If the number of agents in MARL system increases, then system capability and coverage improve, but transmission and computational costs increase

Engineering Contradiction:

Improvesystem capabilityVSAvoidtransmission and computational cost

Core Design Contradiction:

Adaptability or versatilityVSUse of energy by moving object

Solution Approach 1:

The patent extracts only the essential hidden layer representations from each agent's local observations, transmitting only these compressed features rather than raw sensor data or complete state information. This extraction dramatically reduces transmission bandwidth requirements while preserving the critical information needed for collective decision-making.

Inventive Principle:

Principle #2Taking out (Extraction)

Solution Approach 2:

The patent transforms high-dimensional local state observations into fixed-size hidden layer parameter representations through neural network processing. This parameter transformation reduces the dimensionality of transmitted information from potentially thousands of raw sensor values to manageable vector representations, enabling scalable multi-agent systems.

Inventive Principle:

Principle #35Parameter changes

3Loss of information

If complete state information is transmitted between agents, then information completeness is improved, but transmission bandwidth and processing load increase

Engineering Contradiction:

Improveinformation completenessVSAvoidtransmission bandwidth

Core Design Contradiction:

Loss of informationVSQuantity of substance

Solution Approach 1:

The patent creates compressed copies of local state information in the form of hidden layer representations, which are transmitted between agents. These neural network-based copies preserve the essential informational content needed for decision-making while occupying minimal transmission bandwidth, serving as efficient surrogates for complete state data.

Inventive Principle:

Principle #26Copying

Solution Approach 2:

The patent performs preliminary processing of local observations through hidden layer computations before transmission, extracting and encoding only the most relevant features in advance. This preliminary action reduces the information transmission burden by pre-computing and sending only the distilled essential features rather than raw data requiring downstream processing.

Inventive Principle:

Principle #10Preliminary action

Data Source

PatentUS20240330697A1Multi-agent reinforcement learning by receiving and combining hidden layers at each agent

Publication Date: 2024.10.03 HRL LAB

AI summary

A local agent of a multi-agent reinforcement learning (MARL) system is disclosed, the local agent comprising a MARL network comprising at least one local hidden layer responsive to a plurality of local observations. A transmitter is configured to transmit an output of the local hidden layer to at least one remote agent, and a receiver is configured to receive an output of a remote hidden layer from the at least one remote agent. A combiner module is configured to combine the local hidden layer output with the remote hidden layer output to generate a combined hidden layer output, wherein the MARL network is configured to process the combined hidden layer output to generate at least one action value for the local agent.