Parallel Reinforcement Learning Table Partitioning for IoT
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Solution Overview
Problem
Existing parallel reinforcement learning (PRL) algorithms face challenges in reducing execution time due to communication overhead and wasted learning from overlapping search strategies, particularly in resource-constrained distributed IoT environments.
Innovation Solution
A table-based PRL algorithm that enables multiple agents to explore an unexplored domain by receiving assigned partitions of the state-action space, executing actions, granting rewards, exchanging state-action values, and updating tables, thereby reducing communication overhead and improving learning efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If multiple agents use parallel reinforcement learning to explore the state-action space, then the learning time (number of episodes) is reduced, but the execution time (actual clock time) is not reduced due to communication overhead and overlapping search strategies
Solution Approach 1:
The state-action space is divided into multiple partitions, with each agent responsible for exploring a specific partition. This segmentation eliminates overlapping search strategies by assigning distinct regions to different agents, thereby reducing redundant exploration and communication overhead while maintaining parallel learning efficiency
Solution Approach 2:
Before the parallel reinforcement learning process begins, the state-action space is pre-partitioned and assigned to specific agents. This preliminary action ensures that each agent knows its designated exploration region in advance, preventing overlapping searches and reducing communication overhead during the learning process
2Power
If a centralized infrastructure is used to manage IoT devices, then processing power and storage capacity are abundant, but response time and mobility are constrained due to communication requirements
Solution Approach 1:
The centralized infrastructure is segmented into distributed edge computing nodes deployed at network edges. Each node has local processing power and storage capacity, enabling autonomous decision-making without requiring constant communication with the central cloud, thus improving response time while maintaining adequate processing capability
Solution Approach 2:
The architecture transitions from a single-dimensional centralized structure to a multi-dimensional distributed structure, adding spatial distribution as a new dimension. This allows the system to simultaneously achieve fast local response times and access to centralized resources when needed
3Speed
If a distributed infrastructure is used to improve response time and mobility, then communication constraints are reduced, but resource constraints (bandwidth, processing power, storage capacity) are enforced
Solution Approach 1:
The distributed infrastructure is segmented into specialized agents, each with specific resource allocations and responsibilities. This segmentation allows efficient utilization of limited resources by assigning them to specific tasks and agents, reducing overall system complexity despite the distributed nature
Data Source
AI summary
A computer-implemented method for exploring, by a table-based parallel reinforcement learning, PRL, algorithm, an unexplored domain comprising a plurality of agents and states, the unexplored domain represented by a state-action space. The method includes the steps performed by one or more of the plurality of agents receiving an assigned partition of the state-action space represented by a table; and executing during a plurality of episodes actions for states within the partition. An action transits a state; and granting to a transited state a reward; and exchanging state-action values with other agents of the plurality of agents in the domain; and updating the table.