Russell. Algorithms for inverse reinforcement learning. In Proceedings of the International Conference on Machine Learning, 2000.Ng, A., Russell, S.: Algorithms for inverse reinforcement learning. In: Proc. 17th Int. Conf. Machine Learning, pp. 663-670 (2000)...
Implementation of T-REX and D-REX Inverse Reinforcement Learning (IRL) algorithm for learning form suboptimal demonstrations - GitHub - prabinrath/Beyond-Demonstration: Implementation of T-REX and D-REX Inverse Reinforcement Learning (IRL) algorithm for
Again, this is not an Intro to Inverse Reinforcement Learning post, rather it is a tutorial on how to use/code Inverse reinforcement learning framework for your own problem, but IRL lies at the very core of it, and it is quintessential to know about it first. IRL has been extensively stu...
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In order to solve the inverse kinematics (IK) of complex manipulators efficiently, a hybrid equilibrium optimizer slime mould algorithm (EOSMA) is proposed. Firstly, the concentration update operator of the equilibrium optimizer is used to guide the anisotropic search of the slime mould algorithm to...
Different types of technology have been proposed, including rule-based approaches [42,43], optimization techniques [13], probabilistic graphical models [22,23,26,65], inductive logic programming [57,66], aggregation rules [59,63,67], and value alignment through inverse reinforcement learning [68...
Extreme learning machine Higher-order neuron Genetic algorithm Multi-cube neuron Single-layer neural network 1. Introduction The extreme learning machine (ELM) method created by Huang et al. [1], [2] is a training algorithm for single-layer neural networks (SLNNs). Its main advantage is very...
Fixed-Point Algorithms for Inverse Problems in Science and Engineering. Springer Optimization and Its ApplicationsSpringer Optimization and Its Applications, vol. 49, pp. 185–212. Springer, New York (2011) Chapter MATH Google Scholar Dao, M., Tam, M.: A Lyapunov-type approach to convergence ...
The reinforcement learning method has proven to be an effective technique in self-adaptive adjustment, which can help search for better results in large-scale solution space more effectively. In this paper, we propose a large-scale optimization algorithm called reinforcement learning level-based ...
Therefore, a state- less Q-learning algorithm is adopted to solve the service caching problem. Each edge server eNBi is treated as an agent i , its caching decision as an action ai , the inverse of the sum of the collaboration costs of all service instances cached in edge server ...