Multi-Agent Reinforcement Learning With Agent History Encoders
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Solution Overview
Problem
Existing deep Q-network-based methods for multi-agent reinforcement learning face challenges in performing centralized learning and distributed execution due to structural limitations, making it difficult to efficiently handle asynchronous actions in real-world scenarios where agents make independent decisions based on incomplete observations.
Innovation Solution
A system and method utilizing history encoders, including multi-layer perceptrons (MLPs) and gated recurrent units (GRUs), to generate agent-specific history information, combined with an aggregation module for processing these histories, enabling efficient asynchronous multi-agent reinforcement learning by encoding time information and eliminating duplicate observations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If deep Q-network-based methods are used for multi-agent reinforcement learning, then the system can handle basic multi-agent scenarios, but it cannot perform centralized learning and distributed execution due to structural limitations
Solution Approach 1:
The system divides the multi-agent reinforcement learning framework into separate components: individual agent encoders for each agent, a centralized critic module, and distributed actor modules. This segmentation allows the system to perform centralized learning through the critic while enabling distributed execution through the independent actor modules, overcoming the structural limitations of conventional deep Q-network approaches.
Solution Approach 2:
The patent introduces a centralized critic as an intermediary component that receives state information from all agents and computes value functions. This intermediary enables centralized learning by coordinating the distributed agents without requiring them to directly communicate, thus achieving centralized learning and distributed execution simultaneously.
2Ease of operation
If agents make independent decisions based on incomplete observations in asynchronous environments, then the system can operate in real-world scenarios, but it becomes difficult to efficiently handle asynchronous actions
Solution Approach 1:
The system employs history encoders that pre-process and encode sequential observations into compressed history representations before the agents make decisions. This preliminary encoding of historical information allows agents to make informed independent decisions without requiring synchronous communication, thereby提高效率 in handling asynchronous actions while maintaining independent decision-making capability.
Solution Approach 2:
The patent implements dynamic action spaces where agents can perform actions at different time steps based on their local observations and encoded histories. The system dynamically handles asynchronous actions by allowing each agent to act independently when conditions are met, rather than requiring synchronized action execution, thus improving efficiency in asynchronous environments.
3Measurement precision
If history encoders with time information are used for each agent, then the system can accurately encode agent-specific histories, but the device complexity increases
Solution Approach 1:
The patent employs identical history encoder architectures for all agents, where each encoder is designed to process sequential observations and extract temporal features. While multiple encoders are used, they share the same structural design and functionality, which standardizes the system and reduces the complexity burden. The universal encoder design allows accurate encoding of agent-specific histories while maintaining system-wide consistency and manageability.
Data Source
AI summary
A system for multi-agent reinforcement learning includes a multi-agent including a first agent and a second agent, a history encoder including a first history encoder corresponding to the first agent and a second history encoder corresponding to the second agent, a memory configured to store one or more commands, and at least one processor configured to execute the one or more commands stored in the memory, wherein, the at least one processor, by executing the one or more commands, is configured to i) generate first history information of the first agent by inputting observation data of the first agent into the first history encoder and ii) generate second history information of the second agent by inputting observation data of the second agent into the second history encoder.