Results 261 to 270 of about 197,676 (281)

Reinforced mixture learning

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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FUZZY REINFORCEMENT LEARNING

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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Reinforcement Learning

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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Robust Reinforcement Learning

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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Reinforcement learning

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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Meta-learning in Reinforcement Learning

Neural Networks, 2003
Meta-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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Reinforcement learning control

Current Opinion in Neurobiology, 1994
Reinforcement learning refers to improving performance through trial-and-error. Despite recent progress in developing artificial learning systems, including new learning methods for artificial neural networks, most of these systems learn under the tutelage of a knowledgeable 'teacher' able to tell them how to respond to a set of training stimuli ...
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Distributed Reinforcement Learning

Robotics and Autonomous Systems, 1995
In multi-agent systems two forms of learning can be distinguished: centralized learning, that is, learning done by a single agent independent of the other agents; and distributed learning, that is, learning that becomes possible only because several agents are present. Whereas centralized learning has been intensively studied in the field of artificial
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Reinforcement Learning and Deep Reinforcement Learning

2019
In order to better understand state-of-the-art reinforcement learning agent, deep Q-network, a brief review of reinforcement learning and Q-learning are first described. Then recent advances of deep Q-network are presented, and double deep Q-network and dueling deep Q-network that go beyond deep Q-network are also given.
F. Richard Yu, Ying He
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