Multi-Agent Autonomy Planning for Temporal Exploration Control
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Solution Overview
Problem
Current multi-agent reinforcement learning methods, such as actor-critic RL, fail to adequately address the temporal exploration problem, which is crucial for autonomous systems like UAVs, especially in complex and prolonged missions, as they rely on randomizing actions without effectively approximating time, leading to inefficiencies in real-world applications.
Innovation Solution
A neuroevolution-based process is employed, using a deep learning network to train agents in a simulation environment, where parameter vectors are initialized, evaluated, selected, replicated, and mutated to optimize task allocation through the Kuhn-Munkres algorithm, enabling effective control of autonomous platforms by assigning each platform to a target and learning from observations to produce actions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If actor-critic RL methods are used for multi-agent control, then learning capability is provided, but temporal exploration problem occurs leading to inefficiency in prolonged missions
Solution Approach 1:
The patent segments the control architecture into multiple hierarchical levels: high-level task planning, mid-level skill selection, and low-level action execution. This segmentation allows each layer to operate independently with specialized functions, resolving the temporal exploration problem by separating long-term task planning from short-term action selection, thereby improving mission efficiency while maintaining learning capability.
Solution Approach 2:
The patent implements preliminary action through pre-trained skill repositories and predetermined task hierarchies. Instead of randomizing actions at each step, the system pre-learns and stores effective skills and task sequences, then selects from these pre-prepared options during execution. This eliminates the temporal exploration problem by replacing random exploration with intelligent selection from pre-validated action sequences.
2Adaptability or versatility
If random action exploration is used in actor-critic RL, then learning through trial and error is enabled, but time approximation over hundreds or thousands of steps becomes problematic
Solution Approach 1:
The patent applies dynamics by making the exploration strategy adaptive rather than static. The system dynamically adjusts exploration behavior based on the current task phase, confidence levels, and environmental feedback. Instead of uniform random exploration across all time steps, the system intensifies exploration when uncertain and exploits known effective actions when confident, thereby reducing time approximation errors in prolonged missions.
Solution Approach 2:
The patent implements feedback mechanisms where the system continuously monitors task progress, reward signals, and performance metrics to adjust exploration behavior. This feedback loop allows the system to learn from outcomes and refine its action selection strategy over time, reducing reliance on random exploration and improving time approximation accuracy in extended missions.
3Device complexity
If traditional non-learning methods like decision tree analysis are used, then control complexity is reduced, but inability to solve complex autonomous platform control problems occurs
Solution Approach 1:
The patent introduces an intermediary hierarchical structure between simple decision trees and complex autonomous learning systems. The mid-level skill selection layer acts as an intermediary that translates high-level task goals into coordinated low-level actions, bridging the gap between simple control methods and complex learning-based approaches. This intermediary architecture enables complex problem-solving while maintaining manageable control complexity through structured decomposition.
Data Source
AI summary
Described is a system for controlling multiple autonomous platforms. A training process is performed to produce a trained learning agent in a simulation environment. In each episode, each controlled platform is assigned to one target platform that produces an observation. A learning agent processes the observation using a deep learning network and produces an action corresponding to each controlled platform until an action has been produced for each controlled platform. A reward value is obtained corresponding to the episode. The trained learning agent is executed to control each autonomous platform, where the trained agent receives one or more observations from one or more platform sensors and produces an action based on the one or more observations. The action is then used to control one or more platform actuators.