Decentralized Multi-Agent Learning Policy Combination
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Solution Overview
Problem
Existing multi-agent reinforcement learning systems face challenges in decentralized settings, requiring a central controller for value function approximation and often forfeiting permutation invariant latent spaces, which is impractical and inefficient.
Innovation Solution
A decentralized multi-agent learning method that allows intelligent agents to iteratively determine feedback, adjust policies, broadcast and combine policies, and estimate network value functions, using a Stochastic Linearized Augmented Lagrangian Method for robust policy improvement and value function estimation without a central controller.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a centralized controller is used for value function approximation, then the system can achieve coordinated multi-agent learning, but the system complexity increases and scalability decreases
Solution Approach 1:
The patent segments the centralized value function approximation into distributed local approximators at each agent. Each agent maintains its own critic network that approximates the value function locally, eliminating the need for a centralized controller while maintaining coordinated learning through policy gradient updates that consider global rewards.
Solution Approach 2:
The patent extracts the value function approximation capability from a centralized controller and distributes it to individual agents. Each agent independently performs value function approximation using its own critic network, removing the centralized component while preserving the essential function of value estimation for policy improvement.
2Device complexity
If decentralized value function approximation is adopted, then system complexity is reduced, but permutation invariance and homogeneity of the shared network environment are forfeited
Solution Approach 1:
The patent applies local quality by allowing each agent to have its own critic network with potentially different parameters, while the actor networks maintain shared parameters for policies. This enables decentralized value approximation without sacrificing the permutation invariance needed for homogeneous policy learning, as each agent uses its local critic to evaluate actions while all agents follow the same policy structure.
3Measurement precision
If classic centralized training paradigm is used, then value function approximation can be achieved, but it requires a central controller which is impractical in fully decentralized settings
Solution Approach 1:
The patent implements self-service by enabling each agent to independently perform value function approximation using its own critic network without requiring a central controller. Each agent autonomously evaluates the value of states and actions relevant to its local experience, eliminating the impracticality of centralized control while maintaining accurate value estimation for policy improvement.
4Adaptability or versatility
If existing decentralized multi-agent learning methods are used, then agents can learn locally, but convergence speed is slow and training time increases
Solution Approach 1:
The patent enhances feedback mechanisms by having each agent use its local critic network to provide continuous value function estimates during policy gradient updates. This localized feedback loop enables faster convergence compared to traditional decentralized methods, as each agent receives immediate value information from its critic to guide policy improvements without waiting for centralized coordination.
Data Source
AI summary
A computer-implemented method of decentralized multi-agent learning for use in a system having a plurality of intelligent agents each having a personal portion and a shared portion, is provided. The method includes iteratively, until each of a personal goal and a network goal are optimized: determining a feedback associated with an action relative to a personal goal and a degree of similarity relative to a shared goal; adjusting a policy based on the feedback to gain a superior feedback from a next action; broadcasting the shared policy; receiving the at least one of the one or more other intelligent agents' shared policy; generating a combined policy by combining the personal policy and the at least one of the one or more other intelligent agents' shared policy; estimating, using the combined policy, a network value function; and conducting the next action in accordance with the combined policy.