Modified Deep Q Optimizer with Updated SARSA for Improving Learning Efficiency through Optimum Resource Utilization in Reinforcement Learning - Ansari, Vaqar Ahmed; Shah, Kamal; Patil, Rohini; Vasoya, Anil; Saha, Payel; Margaret, Mary; Rajpurohit, Suresh (International Journal of Intelligent Systems and Applications in Engineering, 2024)
In Reinforcement Learning (RL) efficiency of the algorithm is ensured by reducing the cost of learning with maximizing the rewards. In this paper a new technique for RL-based Deep Q optimizers is introduced with Updated SARSA algorithm and newly defined linear cost function. Modified DQ perform significantly faster after learning as it utilize value for particular epoch instead of for the whole dataset .Proposed linear cost model gives wide range of weight parameters “W” where the mean value is always closer to the minimum cost which implies easy to make cluster and train the features. Proposed modified DQ gives 88.2% reduction in variation for relative mean cost for proposed cost-model. With proposed cost model, training execution time for modified DQ has been reduced by 19.35% compared to existing DQ and improves accuracy by 3 to 4% with optimum reward.