We conducted the same experiments with Double Q-learning using two MLPs with the same parameter settings, but without target networks. Experiments have been performed using various maze-solving problems involving deterministic or stochastic transition functions and 2D or 3D grids and two Open-AI gym control problems. Furthermore, we examine if decaying the learning rate over time has advantages over static ones. We focus our analysis on the effect of the learning rate when training the MLP. For solving these tasks, Q-learning is combined with a multilayer perceptron (MLP), experience replay, and a target network.
In this paper, we present a novel analysis of this problem using various control tasks. In Reinforcement learning, Q-learning is the best-known algorithm but it suffers from overestimation bias, which may lead to poor performance or unstable learning. Simulation results show that the CE-based method can improve system availability improvement by up to 32% compared to benchmarking methods, and the Q-learning-based algorithm can further enhance system availability enhancement by up to 20% compared to the proposed CE-based method.
Specifically, the BP neural network is trained using cross-layer simulation data obtained from SPICE simulation while the optimization for system functionality availability is achieved by judiciously selecting an optimal supply voltage for processors under timing constraints. In this paper, we investigate the dynamic modeling of cross-layer soft error rate based on the Back Propagation (BP) neural network, and propose optimization strategies for system availability based on Cross Entropy (CE) and Q-learning algorithms. We present an attack mechanism that uses the portability of competing tests to execute policy incentives and to prove their usefulness and consequences by means of a pilot study of a play learning scenario.Īs the density of integrated circuits continues to increase, the possibility that real-time systems suffer from soft and hard errors rises significantly, resulting in a degraded availability of system functionality. In comparison, all dual Q-learning variables have a significantly higher score compared with Q-learning, and the incremental reward function shows no improved effects than the normal reward function.
The findings indicate that all algorithms are needed to learn how to play successfully. In reality, QL strengthens the benchmark objective with a simple, standardized Q value which, in addition to existing Q-learning and essential applications, is quickly applied. In principle, we demonstrate that QL creates a lower relation to current policy importance and that this can be correlated with guarantees of political learning theoretical change. We revealed technical reinforcement learning in this study. Q-learning (QL), by learning a conservative Q function that allows a policy to be below the predicted value of the Q function, is introduced by us, which aims to circumvent these restrictions. High difficulty in large-scale real-world implementations is the effective use of large data sets previously obtained in augmented learning algorithms. The topic of RL has achieved a new, complete standard of public opinion. The Reinforcement learning (RL) algorithms solve a wide range of problems we faced. Furthermore both double Q-learning variants obtain significantly higher performances than Q-learning and the progressive reward function does not yield better results than the regular reward function. The results show that all algorithms can be used to successfully learn to play Pac-Xon. Furthermore, we have set up an alternative reward function which presents higher rewards towards the end of a level to try to increase the performance of the algorithms. For training the agent, the use of Q-learning is compared to two double Q-learning variants, the original algorithm and a novel variant. The RL agent consists of a multi-layer perceptron (MLP) that uses a feature representation of the game state through input variables and gives Q-values for each possible action as output. In this paper it is investigated whether a reinforcement learning (RL) agent can successfully learn to play this game. Pac-Xon is an arcade video game in which the player tries to fill a level space by conquering blocks while being threatened by enemies.