Reinforcement Learning Agent Control With Belief Modeling

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Solution Overview

Problem

Computer-implemented agents struggle to effectively achieve their goals in environments with humans and other agents due to the unpredictability of their behavior, as they lack knowledge of the strategies and interactions of these entities.

Innovation Solution

Implementing a reinforcement learning policy and an encoder that learns a latent representation of the agent's belief about other agents in the environment, allowing the agent to adapt and make decisions based on this distribution, even without prior knowledge of their strategies.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Adaptability or versatility

If a computer-implemented agent uses traditional machine learning to decide how to act, then it can learn basic behaviors, but it cannot effectively achieve goals in environments with humans and other agents due to unpredictability of their behavior

Engineering Contradiction:

Improveadaptability to other agentsVSAvoidgoal achievement reliability

Core Design Contradiction:

Adaptability or versatilityVSReliability

Solution Approach 1:

The agent maintains a belief distribution over other agents' strategies and updates this distribution based on observed behaviors. This feedback mechanism allows the agent to adapt its policy based on actual observations of other agents, improving both adaptability and reliability in interactive environments

Inventive Principle:

Principle #23Feedback

Solution Approach 2:

The agent changes its internal representation parameters by learning a belief distribution characterized by parameters (such as mean and variance) that describe other agents' strategies. This parameter-based representation enables the agent to handle uncertainty and adapt to varying behaviors of other agents

Inventive Principle:

Principle #35Parameter changes

2Adaptability or versatility

If the agent learns a belief distribution about other agents, then it can adapt to their behaviors, but the complexity of the system increases due to the need to learn and maintain this distribution

Engineering Contradiction:

Improveability to interact with other agentsVSAvoidsystem complexity

Core Design Contradiction:

Adaptability or versatilityVSDevice complexity

Solution Approach 1:

The belief distribution acts as an intermediary representation between the agent and other agents. Instead of directly modeling complex interactions, the agent uses this intermediate belief state to simplify decision-making while maintaining adaptability to other agents' behaviors

Inventive Principle:

Principle #24Intermediary (Mediator)

Solution Approach 2:

The agent introduces a new dimensional aspect to its state space by incorporating a belief distribution over other agents' strategies. This additional dimension allows the agent to represent uncertainty and adaptability without fundamentally redesigning the entire system architecture

Inventive Principle:

Principle #17Another dimension (Dimensionality change)

Data Source

PatentUS12488278B2Interactive agent and control using reinforcement learning

Publication Date: 2025.12.02 MICROSOFT TECHNOLOGY LICENSING LLC

AI summary

In various examples there is a method performed by a computer-implemented agent in an environment. The method comprises storing a reinforcement learning policy for controlling the computer-implemented agent. The method also comprises storing a distribution as a latent representation of a belief of the computer-implemented agent about at least one other agent in the environment. The method involves executing the computer-implemented agent according to the policy conditioned on parameters characterizing the distribution.