Filtering by
- Creators: Computer Science and Engineering Program
- Creators: Yang, Yezhou
- Member of: Theses and Dissertations
We propose a new strategy for blackjack, BB-Player, which leverages Hidden Markov Models (HMMs) in online planning to sample a normalized predicted deck distribution for a partially-informed distance heuristic. Viterbi learning is applied to the most-likely sampled future sequence in each game state to generate transition and emission matrices for this upcoming sequence. These are then iteratively updated with each observed game on a given deck. Ultimately, this process informs a heuristic to estimate the true symbolic distance left, which allows BB-Player to determine the action with the highest likelihood of winning (by opponent bust or blackjack) and not going bust. We benchmark this strategy against six common card counting strategies from three separate levels of difficulty and a randomized action strategy. On average, BB-Player is observed to beat card-counting strategies in win optimality, attaining a 30.00% expected win percentage, though it falls short of beating state-of-the-art methods.
AI and deep learning workloads are different from the conventional cloud and mobile workloads, with respect to: (1) Computational Intensity, (2) I/O characteristics, and (3) communication pattern. While there is a considerable amount of research activity on the theoretical aspects of AI and Deep Learning algorithms that run with greater efficiency, there are only a few studies on the infrastructural impact of Deep Learning workloads on computing and storage resources in distributed systems.
It is typical to utilize a heterogeneous mixture of CPU and GPU devices to perform training on a neural network. Google Brain has a developed a reinforcement model that can place training operations across a heterogeneous cluster. Though it has only been tested with local devices in a single cluster. This study will explore the method’s capabilities and attempt to apply this method on a cluster with nodes across a network.