scholarly journals Deep Reinforcement Learning in Ice Hockey for Context-Aware Player Evaluation

2018 ◽  
Author(s):  
Guiliang Liu ◽  
Oliver Schulte
Keyword(s):  
Reinforcement Learning ◽  
Empirical Evaluation ◽  
Professional Sports ◽  
Ice Hockey ◽  
New Approach ◽  
The Neural Network ◽  
Overall Performance ◽  
Game Context ◽  
Player Performance ◽  
Q Function

A variety of machine learning models have been proposed to assess the performance of players in professional sports. However, they have only a limited ability to model how player performance depends on the game context. This paper proposes a new approach to capturing game context: we apply Deep Reinforcement Learning (DRL) to learn an action-value Q function from 3M play-by-play events in the National Hockey League (NHL). The neural network representation integrates both continuous context signals and game history, using a possession-based LSTM. The learned Q-function is used to value players' actions under different game contexts. To assess a player's overall performance, we introduce a novel Game Impact Metric (GIM) that aggregates the values of the player's actions. Empirical Evaluation shows GIM is consistent throughout a play season, and correlates highly with standard success measures and future salary.

scholarly journals Inverse Reinforcement Learning for Team Sports: Valuing Actions and Players

2020 ◽  
Author(s):  
Yudong Luo ◽  
Oliver Schulte ◽  
Pascal Poupart
Keyword(s):  
Reinforcement Learning ◽  
Single Agent ◽  
Empirical Evaluation ◽  
Team Sports ◽  
Ice Hockey ◽  
Inverse Reinforcement Learning ◽  
Reward Function ◽  
Player Ranking ◽  
The Impact ◽  
Q Function

A major task of sports analytics is to rank players based on the impact of their actions. Recent methods have applied reinforcement learning (RL) to assess the value of actions from a learned action value or Q-function. A fundamental challenge for estimating action values is that explicit reward signals (goals) are very sparse in many team sports, such as ice hockey and soccer. This paper combines Q-function learning with inverse reinforcement learning (IRL) to provide a novel player ranking method. We treat professional play as expert demonstrations for learning an implicit reward function. Our method alternates single-agent IRL to learn a reward function for multiple agents; we provide a theoretical justification for this procedure. Knowledge transfer is used to combine learned rewards and observed rewards from goals. Empirical evaluation, based on 4.5M play-by-play events in the National Hockey League (NHL), indicates that player ranking using the learned rewards achieves high correlations with standard success measures and temporal consistency throughout a season.

scholarly journals Impact of Dataset Size on Classification Performance: An Empirical Evaluation in the Medical Domain

2021 ◽  
Vol 11 (2) ◽  
pp. 796
Author(s):  
Alhanoof Althnian ◽  
Duaa AlSaeed ◽  
Heyam Al-Baity ◽  
Amani Samha ◽  
Alanoud Bin Dris ◽  
...  
Keyword(s):  
Empirical Evaluation ◽  
Classification Performance ◽  
Support Vector ◽  
Robust Model ◽  
Original Distribution ◽  
C4.5 Decision Tree ◽  
Dataset Size ◽  
Overall Performance ◽  
Medical Domain ◽  
The Impact

Dataset size is considered a major concern in the medical domain, where lack of data is a common occurrence. This study aims to investigate the impact of dataset size on the overall performance of supervised classification models. We examined the performance of six widely-used models in the medical field, including support vector machine (SVM), neural networks (NN), C4.5 decision tree (DT), random forest (RF), adaboost (AB), and naïve Bayes (NB) on eighteen small medical UCI datasets. We further implemented three dataset size reduction scenarios on two large datasets and analyze the performance of the models when trained on each resulting dataset with respect to accuracy, precision, recall, f-score, specificity, and area under the ROC curve (AUC). Our results indicated that the overall performance of classifiers depend on how much a dataset represents the original distribution rather than its size. Moreover, we found that the most robust model for limited medical data is AB and NB, followed by SVM, and then RF and NN, while the least robust model is DT. Furthermore, an interesting observation is that a robust machine learning model to limited dataset does not necessary imply that it provides the best performance compared to other models.

scholarly journals Off-Policy Deep Reinforcement Learning by Bootstrapping the Covariate Shift

2019 ◽  
Vol 33 ◽  
pp. 3647-3655
Author(s):  
Carles Gelada ◽  
Marc G. Bellemare
Keyword(s):  
Reinforcement Learning ◽  
Learning Algorithm ◽  
Theoretical Perspective ◽  
Empirical Evaluation ◽  
Nonlinear Function ◽  
Policy Learning ◽  
Extensive Evaluation ◽  
Probability Simplex ◽  
Performance Gains ◽  
Nonlinear Function Approximation

In this paper we revisit the method of off-policy corrections for reinforcement learning (COP-TD) pioneered by Hallak et al. (2017). Under this method, online updates to the value function are reweighted to avoid divergence issues typical of off-policy learning. While Hallak et al.’s solution is appealing, it cannot easily be transferred to nonlinear function approximation. First, it requires a projection step onto the probability simplex; second, even though the operator describing the expected behavior of the off-policy learning algorithm is convergent, it is not known to be a contraction mapping, and hence, may be more unstable in practice. We address these two issues by introducing a discount factor into COP-TD. We analyze the behavior of discounted COP-TD and find it better behaved from a theoretical perspective. We also propose an alternative soft normalization penalty that can be minimized online and obviates the need for an explicit projection step. We complement our analysis with an empirical evaluation of the two techniques in an off-policy setting on the game Pong from the Atari domain where we find discounted COP-TD to be better behaved in practice than the soft normalization penalty. Finally, we perform a more extensive evaluation of discounted COP-TD in 5 games of the Atari domain, where we find performance gains for our approach.

A New Approach for Classification of Fault in Transmission Line with Combination of Wavelet Multi Resolution Analysis and Neural Networks

2017 ◽  
Vol 8 (1) ◽  
pp. 505
Author(s):  
Y Srinivasa Rao ◽  
G. Ravi Kumar ◽  
G. Kesava Rao
Keyword(s):  
Neural Networks ◽  
Wavelet Transform ◽  
Transmission Line ◽  
Discrete Wavelet Transform ◽  
Discrete Wavelet ◽  
New Approach ◽  
The Neural Network ◽  
Multi Resolution Analysis ◽  
Resolution Analysis

An appropriate fault detection and classification of power system transmission line using discrete wavelet transform and artificial neural networks is performed in this paper. The analysis is carried out by applying discrete wavelet transform for obtained fault phase currents. The work represented in this paper are mainly concentrated on classification of fault and this classification is done based on the obtained energy values after applying discrete wavelet transform by taking this values as an input for the neural network. The proposed system and analysis is carried out in Matlab Simulink.

scholarly journals Dialogue Generation: From Imitation Learning to Inverse Reinforcement Learning

2019 ◽  
Vol 33 ◽  
pp. 6722-6729 ◽  
Author(s):  
Ziming Li ◽  
Julia Kiseleva ◽  
Maarten De Rijke
Keyword(s):  
Reinforcement Learning ◽  
State Of The Art ◽  
The State ◽  
Experimental Results ◽  
Imitation Learning ◽  
Local Optimum ◽  
Inverse Reinforcement Learning ◽  
High Quality ◽  
Overall Performance

The performance of adversarial dialogue generation models relies on the quality of the reward signal produced by the discriminator. The reward signal from a poor discriminator can be very sparse and unstable, which may lead the generator to fall into a local optimum or to produce nonsense replies. To alleviate the first problem, we first extend a recently proposed adversarial dialogue generation method to an adversarial imitation learning solution. Then, in the framework of adversarial inverse reinforcement learning, we propose a new reward model for dialogue generation that can provide a more accurate and precise reward signal for generator training. We evaluate the performance of the resulting model with automatic metrics and human evaluations in two annotation settings. Our experimental results demonstrate that our model can generate more high-quality responses and achieve higher overall performance than the state-of-the-art.

Sports Rescue: The South End Mustangs Professional Ice Hockey Team

2015 ◽  
Vol 4 (1) ◽  
pp. 44-53
Author(s):  
Chris Chard ◽  
Kirsty K. Spence
Keyword(s):  
United Kingdom ◽  
Professional Sports ◽  
Ice Hockey ◽  
Professional Sport ◽  
Business Owner ◽  
Television Show ◽  
The South ◽  
The United Kingdom ◽  
Sports Team

Three years ago, Steve Thornton purchased the South End Mustangs, a professional ice hockey team competing in the D1 division in the United Kingdom. Unfortunately, Thornton has experienced challenging times during his ownership tenure. The team has achieved mediocre results on the ice and poor results off the ice. Thornton knows he needs help to turn the Mustangs franchise around. Thus, as a result, he turns to John Tapner, a sport business owner, operator, entrepreneur, and advisor. Tapner is best known as a professional sport consultant and TV personality, representing his company Sports Rescue, which is the same name as his hit television show. When an owner calls Tapner, it is because a professional sports team is in trouble and needs to be rescued.

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