Decentralized reinforcement learning (DRL) has been applied to a number of distributed applications. However, one of the main challenges faced by DRL is its convergence. Previous ...
Chongjie Zhang, Victor R. Lesser, Sherief Abdallah
To deal with the increasing complexity of software systems and uncertainty of their environments, software engineers have turned to self-adaptivity. Self-adaptive systems are capab...
Yuriy Brun, Giovanna Di Marzo Serugendo, Cristina ...
Greedy search is commonly used in an attempt to generate solutions quickly at the expense of completeness and optimality. In this work, we consider learning sets of weighted actio...
PAC-MDP algorithms approach the exploration-exploitation problem of reinforcement learning agents in an effective way which guarantees that with high probability, the algorithm pe...
Applying Vector Autoregression (VAR) and genetic algorithm (GA) in hybrid systems with neural network can improve the NN's prediction capability. Two case studies have been ca...