Results 301 to 310 of about 171,350 (313)
Some of the next articles are maybe not open access.
2021
In this paper we focus on reinforcement learning algorithms that are sensitive to risk. The notion of risk we work with is the well-known conditional value-at-risk (CVaR). We describe a faster method for computing value iteration updates for CVaR markov decision processes (MDP). This improvement then opens doors for a sampling version of the algorithm,
Silvestr Stanko, Karel Macek
openaire +2 more sources
In this paper we focus on reinforcement learning algorithms that are sensitive to risk. The notion of risk we work with is the well-known conditional value-at-risk (CVaR). We describe a faster method for computing value iteration updates for CVaR markov decision processes (MDP). This improvement then opens doors for a sampling version of the algorithm,
Silvestr Stanko, Karel Macek
openaire +2 more sources
IEEE/WIC International Conference on Intelligent Agent Technology, 2003. IAT 2003., 2004
Reinforcement learning is the problem faced by a controller that must learn behavior through trial and error interactions with a dynamic environment. The controller's goal is to maximize reward over time, by producing an effective mapping of states of actions called policy.
Hironori Hirata, Fei Qian
openaire +2 more sources
Reinforcement learning is the problem faced by a controller that must learn behavior through trial and error interactions with a dynamic environment. The controller's goal is to maximize reward over time, by producing an effective mapping of states of actions called policy.
Hironori Hirata, Fei Qian
openaire +2 more sources
Fuzzy Q-learning and dynamical fuzzy Q-learning
Proceedings of 1994 IEEE 3rd International Fuzzy Systems Conference, 2002This paper proposes two reinforcement-based learning algorithms: 1) fuzzy Q-learning in an adaptation of Watkins' Q-learning for fuzzy inference systems; and 2) dynamical fuzzy Q-learning which eliminates some drawbacks of both Q-learning and fuzzy Q-learning. These algorithms are used to improve the rule base of a fuzzy controller. >
openaire +2 more sources
, 2020 Machine learning (ML) is aimed at autonomous extraction of knowledge from raw real-world data or exemplar instances. Machine learning (Barreno et al. in Proceedings of the 2006 ACM symposium on information, computer and communications security, pp 16–25, 2006 [1]) matches the learned pattern with the objects and predicts the outcome.
Jaya Sil, Nandita Sengupta
openaire +1 more source
Proceedings of 6th International Fuzzy Systems Conference, 2002
This paper proposes an adaptation of Watkins' Q-learning (1989, 1992) for fuzzy inference systems where both the actions and the Q-functions are inferred from fuzzy rules. This approach is compared with genetic algorithm on the cart-centering problem, showing its effectiveness.
Lionel Jouffe, P.Y. Glorennec
openaire +2 more sources
This paper proposes an adaptation of Watkins' Q-learning (1989, 1992) for fuzzy inference systems where both the actions and the Q-functions are inferred from fuzzy rules. This approach is compared with genetic algorithm on the cart-centering problem, showing its effectiveness.
Lionel Jouffe, P.Y. Glorennec
openaire +2 more sources
Q-learning for Statically Scheduling DAGs
2020 IEEE International Conference on Big Data (Big Data), 2020Data parallel frameworks (e.g. Hive, Spark or Tez) can be used to execute complex data analyses consisting of many dependent tasks represented by a Directed Acylical Graph (DAG). Minimising the job completion time (i.e. makespan) is still an open problem for large graphs.We propose a novel deep Q-learning (DQN) approach to statically scheduling DAGs ...
Roeder, Julius +2 more
openaire +2 more sources
2021
In this chapter, we will do a deep dive into Q-learning combined with function approximation using neural networks. Q-learning in the context of deep learning using neural networks is also known as Deep Q Networks (DQN). We will first summarize what we have talked about so far with respect to Q-learning. We will then look at code implementations of DQN
openaire +2 more sources
In this chapter, we will do a deep dive into Q-learning combined with function approximation using neural networks. Q-learning in the context of deep learning using neural networks is also known as Deep Q Networks (DQN). We will first summarize what we have talked about so far with respect to Q-learning. We will then look at code implementations of DQN
openaire +2 more sources
2018
In order to solve the problem that Q-learning can suffer from large overestimations in some stochastic environments, we first propose a new form of Q-learning, which proves that it is equivalent to the incremental form and analyze the reasons why the convergence rate of Q-learning will be affected by positive bias.
Zhihui Hu +4 more
openaire +2 more sources
In order to solve the problem that Q-learning can suffer from large overestimations in some stochastic environments, we first propose a new form of Q-learning, which proves that it is equivalent to the incremental form and analyze the reasons why the convergence rate of Q-learning will be affected by positive bias.
Zhihui Hu +4 more
openaire +2 more sources
Q-learning in a competitive supply chain
2007 IEEE International Conference on Systems, Man and Cybernetics, 2007The participants in a competitive supply chain take their decisions individually in a distributed environment and independent of one another. At the same time, they must coordinate their actions so that the total profitability of the supply chain is safeguarded.
Van Tongeren, T. +3 more
openaire +5 more sources
Enhancing Nash Q-learning and Team Q-learning mechanisms by using bottlenecks
Journal of Intelligent & Fuzzy Systems, 2014Nash Q-learning and team Q-learning are extended versions of reinforcement learning method for using in Multi-agent systems as cooperation mechanisms. The complexity of multi-agent reinforcement learning systems is extremely high thus it is necessary to use complexity reduction methods like hierarchical structures, abstraction and task decomposition. A
Nasser Mozayani, Behzad Ghazanfari
openaire +2 more sources