Results 221 to 230 of about 640,433 (264)
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Neural Networks, 2023
In this article, we formulate the standard mixture learning problem as a Markov Decision Process (MDP). We theoretically show that the objective value of the MDP is equivalent to the log-likelihood of the observed data with a slightly different parameter space constrained by the policy.
Yuan Le, Fan Zhou, Yang Bai
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In this article, we formulate the standard mixture learning problem as a Markov Decision Process (MDP). We theoretically show that the objective value of the MDP is equivalent to the log-likelihood of the observed data with a slightly different parameter space constrained by the policy.
Yuan Le, Fan Zhou, Yang Bai
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International Journal of Modern Physics C, 2002
Fuzzy logic represents an extension of classical logic, giving modes of approximate reasoning in an environment of uncertainty and imprecision. Fuzzy inference systems incorporates human knowledge into their knowledge base on the conclusions of the fuzzy rules, which are affected by subjective decisions.
Andrecut, M., Ali, M. K.
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Fuzzy logic represents an extension of classical logic, giving modes of approximate reasoning in an environment of uncertainty and imprecision. Fuzzy inference systems incorporates human knowledge into their knowledge base on the conclusions of the fuzzy rules, which are affected by subjective decisions.
Andrecut, M., Ali, M. K.
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In this chapter, reinforcement learning (RL), a subfield of machine learning that has gained prominence because it enables agents to interact with their surroundings and learn from their mistakes, is covered in great detail. The chapter looks at the core elements of RL, including agents, actions, states, and rewards, in addition to examining a number ...
Arti Saxena, Falak Bhardwaj
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Arti Saxena, Falak Bhardwaj
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Neural Computation, 2005
This letter proposes a new reinforcement learning (RL) paradigm that explicitly takes into account input disturbance as well as modeling errors. The use of environmental models in RL is quite popular for both off-line learning using simulations and for online action planning.
Jun, Morimoto, Kenji, Doya
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This letter proposes a new reinforcement learning (RL) paradigm that explicitly takes into account input disturbance as well as modeling errors. The use of environmental models in RL is quite popular for both off-line learning using simulations and for online action planning.
Jun, Morimoto, Kenji, Doya
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Proceedings of the 2016 Conference on User Modeling Adaptation and Personalization, 2016
Reinforcement Learning (RL) is one of the best machine learning approaches for decision making in interactive environments. RL focuses on inducing effective decision making policies with the goal of maximizing the agent's cumulative reward. In this study, we investigated the impact of both immediate and delayed reward functions on RL-induced policies ...
Shitian Shen, Min Chi
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Reinforcement Learning (RL) is one of the best machine learning approaches for decision making in interactive environments. RL focuses on inducing effective decision making policies with the goal of maximizing the agent's cumulative reward. In this study, we investigated the impact of both immediate and delayed reward functions on RL-induced policies ...
Shitian Shen, Min Chi
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Meta-learning in Reinforcement Learning
Neural Networks, 2003Meta-parameters in reinforcement learning should be tuned to the environmental dynamics and the animal performance. Here, we propose a biologically plausible meta-reinforcement learning algorithm for tuning these meta-parameters in a dynamic, adaptive manner.
Nicolas, Schweighofer, Kenji, Doya
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