Multi-Agent Meta-Reinforcement Learning for Faster Task Adaptation
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Solution Overview
Problem
Existing reinforcement learning methods struggle with multi-agent environments, where multiple agents decide actions based on their own information and policies for cooperative or competitive goals, leading to challenges in convergence, scalability, and efficiency, especially when adapting to new tasks.
Innovation Solution
Introduce meta-learning elements into multi-agent reinforcement learning by defining local groups and states, learning latent vectors, and strategies using variational autoencoders to improve adaptation and convergence speed while maintaining generalization performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If multi-agent reinforcement learning is applied to cooperative or competitive environments, then the system can handle complex multi-agent decision-making, but convergence speed and adaptation to new tasks deteriorate
Solution Approach 1:
The patent applies meta-learning to perform preliminary learning on source tasks before adapting to target tasks. The multi-agent system pre-learns policies and strategies from source tasks, which are then transferred to accelerate adaptation to new target tasks, reducing the time needed for convergence on unseen tasks.
Solution Approach 2:
The patent uses policy copying and transfer between source and target tasks. Learned policies from source tasks are copied and adapted to target tasks, allowing the system to leverage previously acquired knowledge rather than learning from scratch, thus improving adaptation speed while maintaining convergence performance.
2Adaptability or versatility
If multi-agent reinforcement learning is applied to cooperative or competitive environments, then the system can handle complex multi-agent decision-making, but learning efficiency deteriorates
Solution Approach 1:
The patent creates a universal multi-agent policy that can function across multiple different tasks and environments. By learning a generalized policy through meta-learning that adapts to various source and target tasks, the system achieves multi-functionality, expanding the range of solvable tasks while improving learning efficiency through knowledge transfer.
Solution Approach 2:
The patent copies learned policies and experiences from source tasks to target tasks, improving learning efficiency by avoiding redundant learning. This copying mechanism allows the system to quickly adapt to new tasks by leveraging previously acquired knowledge, thus expanding the range of solvable tasks without proportionally increasing learning time.
3Ease of operation
If traditional reinforcement learning is used for single-agent tasks, then learning is simpler and faster, but it cannot handle multi-agent cooperative or competitive scenarios
Solution Approach 1:
The patent segments the complex multi-agent learning problem into manageable components by defining local groups and local states for each agent. This segmentation allows each agent to learn its policy independently based on local information, maintaining learning simplicity while enabling multi-agent capability through coordinated local decisions.
Solution Approach 2:
The patent introduces meta-learning as an intermediary layer that bridges simple single-agent learning and complex multi-agent scenarios. The meta-learning component learns to adapt policies for multi-agent environments while keeping the underlying agent policies relatively simple, thus maintaining ease of operation while achieving multi-agent capability.
Data Source
AI summary
Disclosed is a method and apparatus for performing multi-agent meta reinforcement learning. The method for performing multi-agent meta reinforcement learning may include: selecting an event by extracting trajectory information for a task for pre-learning; defining a local group and a local state including one or more agents based on the selected event; learning a latent vector based on the defined local group and local state; and learning a strategy based on the latent vector and an action of the one or more agents.