On the Connectivity between Reinforcement Learning and the Game of Go – We study the problem of learning the goal-directed behavior of a person through online games. In addition to the traditional games, we propose a new game: the game of Go, where the user has to find a way to navigate a given environment and perform a task in an online manner. By using the game in conjunction with the goal of exploration we observe that users are naturally motivated by the task of finding a path to victory.

This paper addresses the problem of determining the best match between two-way and two-player online strategies. This problem was proposed in the paper’s article ‘Online-Hierarchical Coaching’, which is based on online learning of strategies to minimize regret (or reward) for certain actions, where a player plays a game and a two-player opponent plays a game. Our approach is a modification of the traditional reinforcement learning model in which a player chooses the optimal strategy from a list of actions on the list of available actions, and the opponent chooses the best option (this model is referred to as reinforcement learning. We propose a new algorithm, Neural Coaching, which is capable of predicting the outcomes using a set of agents. Our method outperforms the best existing reinforcement learning algorithms for both playing the two-player game and predicting outcomes from the list of available actions when the two-player game is played.

A deep residual network for event prediction

On the Geometry of Optimal Algorithms for Generalized Support Vector Machines

On the Connectivity between Reinforcement Learning and the Game of Go

Neural image segmentation: boosting efficiency in non-rigid registration

Using a Convolutional Restricted Boltzmann Machine to Detect and Track Multiple TargetsThis paper addresses the problem of determining the best match between two-way and two-player online strategies. This problem was proposed in the paper’s article ‘Online-Hierarchical Coaching’, which is based on online learning of strategies to minimize regret (or reward) for certain actions, where a player plays a game and a two-player opponent plays a game. Our approach is a modification of the traditional reinforcement learning model in which a player chooses the optimal strategy from a list of actions on the list of available actions, and the opponent chooses the best option (this model is referred to as reinforcement learning. We propose a new algorithm, Neural Coaching, which is capable of predicting the outcomes using a set of agents. Our method outperforms the best existing reinforcement learning algorithms for both playing the two-player game and predicting outcomes from the list of available actions when the two-player game is played.