Multi-Agent Reinforcement Learning for Noisy Environment Robustness
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Solution Overview
Problem
Reinforcement learning models face challenges in accurately evaluating action strategies in noisy environments, leading to erratic behavior and reduced performance, especially when transitioning from noise-free to noisy conditions.
Innovation Solution
A multi-agent-based reinforcement learning system is introduced, where a master agent performs reinforcement learning in one environment and shares its experience with slave agents in another, using their value functions to improve learning robustness and efficiency, employing randomized ensembled double Q-learning and double Q-learning algorithms.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If a single reinforcement learning agent learns in a noise-free environment, then learning efficiency is high, but the agent exhibits erratic behavior and reduced performance in noisy environments
Solution Approach 1:
The system divides the reinforcement learning task into multiple independent agents, each learning in parallel. The master agent coordinates multiple slave agents, allowing the system to process multiple experience streams simultaneously, which improves both learning efficiency and robustness to noise through diversity of experience
Solution Approach 2:
The system merges the Q-functions of multiple slave agents with the master agent's Q-function through experience sharing. By combining experiences from multiple agents that have learned in different environments, the system creates a more robust policy that generalizes better to noisy conditions while maintaining high learning efficiency
2Adaptability or versatility
If reinforcement learning uses trial-and-error in unfamiliar environments, then the agent can adapt to new conditions, but it cannot accurately evaluate state values and takes erratic actions
Solution Approach 1:
Slave agents perform preliminary learning in their respective environments before the master agent synthesizes their experiences. This preliminary action allows agents to gather experience in controlled settings, which is then combined to improve state value evaluation accuracy when facing unfamiliar noisy environments
Solution Approach 2:
The master agent acts as an intermediary that collects and integrates experiences from multiple slave agents. This intermediary role allows the system to accurately evaluate state values by aggregating information from multiple sources before making decisions in unfamiliar environments, reducing erratic behavior
Data Source
AI summary
Disclosed are a multi-agent-based reinforcement learning system and method therefor. The multi-agent-based reinforcement learning system includes: a slave agent configured to: store a data set collected in each state of a first environment in a first buffer, store a data set received from a master agent in the first buffer, and learn a Q-function based on the data set stored in the first buffer; and the master agent configured to store a data set collected in each state of a second environment in a second buffer; transmit the data set to the slave agent; update a Q-function matched with the slave agent among a plurality of Q-functions; and perform reinforcement learning based on the data set stored in the second buffer.