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.
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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. Create custom units, or even custom game play modes, and share those as well.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. Use the map editor to make your own maps for fun and sharing.
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Play the story missions, the community crafted missions, or use the built in mission generator for infinite possibilities.
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Alternately, play massive maps in real-time with full save support.
Play small maps casually, while pausing and issuing orders. Guide a scientist and cohorts through the ultimate test of survival and restore hope to the galaxy. The galaxy once again finds itself culled and utterly in ruins.
And, if you last long enough, launch orbital weapons and call down devastating strikes on the enemy.Ĭreeper World 4 continues the iconic and well received Creeper World RTS franchise, expanding into a new dimension of strategic possibilities and threats. Increase your lines of sight and range on high ground, but beware the aerial units. Take the high ground with your forces to avoid the creeper as waves crash around your base. Assemble your forces and struggle against the creeper on all fronts as it floods and fills the map. Build out your economic base with energy and mined wares. The eternal harvester of galactic empires has returned! Witness massive waves of Creeper flood across the 3D terrain in this real time strategy game where the enemy is a fluid.