Analysis on Improving the Response Time with PIDSARSA-RAL in ClowdFlows Mining Platform

Abstract

This paper provides an improved parallel data processing in Big Data mining using ClowdFlows platform. The big data processing involves an improvement in Proportional Integral Derivative (PID) controller using Reinforcement Adaptive Learning (RAL). The Reinforcement Adaptive Learning involves the use of Actor-critic State-action-reward-state-action (SARSA) learning that suits well the stream mining module of ClowdFlows platform. The study concentrates on batch mode processing in Big Data mining model with the use of proposed PID-SARSA-RAL. The experimental evaluation with the conventional ClowdFlows platform proved the effectiveness of the proposed method over continuous parallel workflow execution.

Routing using reinforcement learning in vehicular ad hoc networks

“…Then the agent receives a reward ( r t ) or penalty ( p t ) that signifies whether the action is good or bad. From these state (S) and action (A), the mapping ( π ) is learnt by DRL (π: S → A ), where the improved model for state and action is referred from Reference .…”

Design and Comparison of Reinforcement-Learning-Based Time-Varying PID Controllers with Gain-Scheduled Actions

“…Conventionally, Sarsa and Q-learning have been applied to robotics problems [11]. Recently, a fuzzy Sarsa learning (known as FSL) algorithm was proposed to control a biped walking robot [12] and a PID-SARSA-RAL algorithm was proposed in the ClowdFlows platform to improve parallel data processing [13]. Q-learning is often applied in combination with a proportional integral derivative (PID) controller.…”

“…Recently, some relevant applications of model-free reinforcement learning for tracking problems can be found in [20,21]. In these results [12][13][14][15][16][17][18], the PID controller has attracted substantial attention as an application of reinforcement learning algorithms [13][14][15][16]. However, the literature [13,[15][16][17][18] has provided optimal but static gains in control, and a complete search of the Q-table is required in [14].…”

“…In these results [12][13][14][15][16][17][18], the PID controller has attracted substantial attention as an application of reinforcement learning algorithms [13][14][15][16]. However, the literature [13,[15][16][17][18] has provided optimal but static gains in control, and a complete search of the Q-table is required in [14]. Moreover, most of the results [12][13][14][16][17][18][19] were validated in a simulated environment.…”

“…However, the literature [13,[15][16][17][18] has provided optimal but static gains in control, and a complete search of the Q-table is required in [14]. Moreover, most of the results [12][13][14][16][17][18][19] were validated in a simulated environment.…”