Layered Multi-Agent Continuous Control for Temporal Logic Tasks
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Solution Overview
Problem
Existing temporal equilibrium analysis methods for multi-agent systems face challenges such as double-exponential time complexity, especially under imperfect information conditions, and are not scalable for large-scale multi-agent systems with high-dimensional state and action spaces.
Innovation Solution
A multi-agent multi-task layered continuous control method based on temporal equilibrium analysis, which includes constructing a multi-agent multi-task game model, performing temporal equilibrium analysis, synthesizing top-level control policies, and connecting these policies with deep deterministic policy gradient algorithms for continuous task control.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If temporal equilibrium analysis is applied to generate top-level control policies for multi-agent systems, then the ability to express complex task constraints and capture temporal attributes is improved, but the time complexity becomes double-exponential and the system becomes unsuitable for large-scale multi-agent systems
Solution Approach 1:
The patent segments the control system into two distinct layers: a top layer responsible for temporal equilibrium analysis that generates abstract control policies, and a bottom layer that executes continuous control actions. This segmentation allows the computationally intensive temporal analysis to operate on abstracted representations rather than raw continuous states, reducing the effective complexity while maintaining specification accuracy.
Solution Approach 2:
The patent introduces an intermediary representation layer that translates complex temporal logic specifications into intermediate control policies. This intermediary layer acts as a bridge between the high-level temporal equilibrium analysis and the low-level continuous control execution, enabling the system to handle large-scale multi-agent scenarios by processing information at multiple abstraction levels rather than directly.
2Adaptability or versatility
If policy-gradient based algorithms are used for continuous control with continuous state and action spaces, then the capability to handle continuous control tasks is improved, but the algorithm suffers from sparse rewards, overestimation, and local optima trapping reducing scalability
Solution Approach 1:
The patent applies preliminary action by first generating top-level control policies through temporal equilibrium analysis before executing continuous control tasks. This preliminary policy generation provides a structured framework that guides the subsequent continuous control process, preventing policy-gradient algorithms from wandering in high-dimensional spaces and reducing the risk of getting trapped in local optima.
Solution Approach 2:
The patent implements a feedback mechanism where the outcomes of continuous control execution are fed back to refine and update the top-level control policies. This multi-level feedback loop allows the system to learn from actual execution results and continuously improve both the abstract policy layer and the continuous control layer, addressing the sparse reward problem by providing intermediate feedback signals.
Data Source
AI summary
The present invention discloses a temporal equilibrium analysis-based multi-agent multi-task continuous control method, comprising steps: constructing a multi-agent multi-task game model based on temporal logic, performing temporal equilibrium analysis, and synthesizing multi-agent top-level control policies; constructing a specification auto-completion mechanism, improving dependent task specification by adding environment assumptions; constructing connection mechanism between the top-level control policies and bottom-level deep deterministic policy gradient algorithms, and constructing multi-agent continuous task controllers based on the connection mechanism. The present invention captures the temporal attributes of tasks based on temporal logic, improves the interpretability and usability of system specification through specification completion, and generate top-level abstract task representations and apply them to the control of bottom-level continuous systems, solving the practical problems on multi-agent multi-task continuous control such as poor scalability, easy to fall into local optimality and sparse rewards.