Unsupervised Learning with Randomized Labelings


Unsupervised Learning with Randomized Labelings – Randomization is generally regarded as a problem of finding an optimal policy that optimizes the information for a given policy. In this paper, we explore how randomized policy optimization can be performed by minimizing the cost function of an unknown policy in terms of the objective function itself, under the assumption that the policy optimizes in the expected (or the unobserved) direction. The expected cost function itself can provide an information-theoretic explanation for this knowledge-theoretic assumption, and thus provides a framework and empirical results for estimating cost functions for unknown policy optimization problems.

This paper proposes an experimental evaluation of Reinforcement Learning (RL). Our primary goal is to evaluate how the RL algorithm performs in a game for simulated human and machine learning applications. We propose a novel approach for RL algorithms that leverages the structure of the game and the environment in order to improve performance. Our experimental results show that RL algorithms improve performance in both simulated and real world games where RL algorithms play a significant role. We show that RL algorithms are effective and show that it is an efficient way to enhance performance of RL algorithms in real-valued games, both on the game-stage and in the real world.

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Unsupervised Learning with Randomized Labelings

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  • Design of Novel Inter-rater Agreement and Boundary Detection Using Nonmonotonic Constraints

    An Empirical Evaluation of Reinforcement LearningThis paper proposes an experimental evaluation of Reinforcement Learning (RL). Our primary goal is to evaluate how the RL algorithm performs in a game for simulated human and machine learning applications. We propose a novel approach for RL algorithms that leverages the structure of the game and the environment in order to improve performance. Our experimental results show that RL algorithms improve performance in both simulated and real world games where RL algorithms play a significant role. We show that RL algorithms are effective and show that it is an efficient way to enhance performance of RL algorithms in real-valued games, both on the game-stage and in the real world.


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