… • Differently from prior approaches, our metrics are based on the accumulated reward of
suitable Markov Chains. The intuition is that the accumulated reward, with each node reward …

… algorithm, named MCRR (Markov Chain with Rewards Ranking) for the VNE … reward of
a suitable Markov Chain. The intuition is that the accumulated reward, with each node reward …

… Following [6], for MCRR-LA we adopt a value of γ = 0.98 (the discount factor of the Markov
Reward Model). In the implementation of the QoS-RS algorithm, we do not consider the node …

… reward and multiple actions, where the state of each arm evolves according to a Markov
decision process (MDP), and the reward … makings, rather than using a heuristic one or black-box …

… reward function, but as the problems of minimizing costs and maximizing rewards are equivalent
we use reward-… Almost all algorithms we are aware of perform Monte-Carlo sampling, ie …

… In other words, a reward received by the … heuristic accelerated Markov games with XCS
(HAMXCS) to solve competitive Markov games, which incorporates provided rough heuristic …

… to accelerate the discovery of rewards. By maximizing the … Under the sparse-reward setting,
we show that the proposed … to learn winning strategies under the sparse-reward setting. …

… the first heuristic search algorithm for … -aware behaviors called Observer-Aware MDP (OAMDP)
(Miura and Zilberstein 2021). OAMDP is a variant of MDP with non-Markovian rewards, …

… on the formal concept that is known as a Markov Decision Process (MDP). Literature on …
of an MDP’s reward function by specification of a set of factored reward functions. In a finite-…

… not aware of any regret lower bound for the online Frank-Wolfe algorithm that involves β. …
with existing heuristics on reinforcement learning algorithms assuming scalar rewards for …