Multi-Agent Collaboration Control for Dynamic Agent Openness
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Solution Overview
Problem
Existing multi-agent systems, particularly human-robot collaboration (HRC) systems, face challenges in modeling agent openness and adaptability, as traditional methods fail to account for dynamic agent entry and exit, leading to inefficiencies and inflexibility in real-world scenarios.
Innovation Solution
A decentralized Markov decision process (Dec-MDP) is adapted to include a collaboration variable and additional actions for agent activation and deactivation, forming an open Dec-MDP (oDec-MDP), which is trained using reinforcement learning and inverse reinforcement learning to manage agent openness and optimize collaboration.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If traditional closed multi-agent systems are used, then system structure is simple and predictable, but agent adaptability and flexibility are reduced when agents need to dynamically join or leave tasks
Solution Approach 1:
The patent transforms the static agent participation structure into a dynamic one by introducing the collaboration variable that enables agents to transition between active and inactive states. This allows the system to adapt its composition based on task requirements while maintaining a manageable underlying structure through the formalized state transition mechanisms.
Solution Approach 2:
The patent segments the agent participation control into distinct components: the collaboration variable, activation actions, and deactivation actions. This segmentation allows independent management of agent entry and exit processes, making the overall system more adaptable without proportionally increasing complexity.
2Loss of information
If the world state is tracked to model agent openness, then complete system state information is available, but redundancy increases due to tracking attributes affected by inactive agents
Solution Approach 1:
The patent extracts only the essential collaboration variable from the complete world state, separating the critical information needed for agent openness modeling from redundant attributes. This extraction maintains necessary state information while eliminating unnecessary data about inactive agents, reducing overall state complexity.
3Productivity
If reinforcement learning and inverse reinforcement learning are used to train the oDec-MDP, then collaboration optimization is achieved, but training complexity and computational resources increase
Solution Approach 1:
The patent applies preliminary action by using inverse reinforcement learning to first infer the reward function from expert demonstrations before applying reinforcement learning for policy optimization. This preliminary step provides a principled foundation that guides subsequent training, improving overall efficiency despite the added computational step.
Solution Approach 2:
The patent implements feedback mechanisms through both IRL (inferring reward from expert behavior) and RL (optimizing policy based on reward feedback). This dual feedback approach enables the system to learn optimal collaboration strategies while adapting to dynamic agent participation, achieving high productivity through iterative improvement.
Data Source
AI summary
Embodiments disclosing a controller for controlling a collaboration of a set of agents jointly performing a task are provided. The set of agents includes different combinations of active agents and inactive agents defined by a collaboration variable. The controller is configured to accept a feedback signal including observations of a state of execution of the task performed by active agents, as specified in the collaboration variable. The observations are processed with a neural network trained with machine learning to determine actions for the active agents. The actions include one or more activation actions that cause activation or deactivation of a specific agent from the set of agents. The collaboration variable is updated when the neural network outputs at least one activation action to update a combination of active and inactive agents and cause the active agents to execute the determined actions.