Related papers: Reinforcement Learning for Assignment problem

Reinforcement Learning for Assignment problem

URL: http://arxiv.org/abs/2011.03909v1
Date: Sun, 8 Nov 2020 06:25:50 GMT
Title: Reinforcement Learning for Assignment problem
Authors: Filipp Skomorokhov (1 and 2) and George Ovchinnikov (2) ((1) Moscow Institute of Physics and Technology, (2) Skolkovo Institute of Science and Technology)
Abstract summary: Our simulator resembles real world problems by means of changes in environment. We applied Q-learning based method to the number of dynamic simulations and outperformed analytical greedy-based solution in terms of total reward.
Score: 0.0
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Abstract: This paper is dedicated to the application of reinforcement learning combined with neural networks to the general formulation of user scheduling problem. Our simulator resembles real world problems by means of stochastic changes in environment. We applied Q-learning based method to the number of dynamic simulations and outperformed analytical greedy-based solution in terms of total reward, the aim of which is to get the lowest possible penalty throughout simulation.

Related papers

Efficient Imitation Learning with Conservative World Models [54.52140201148341]
We tackle the problem of policy learning from expert demonstrations without a reward function. We re-frame imitation learning as a fine-tuning problem, rather than a pure reinforcement learning one.
arXiv Detail & Related papers (2024-05-21T20:53:18Z)
Deep multitask neural networks for solving some stochastic optimal control problems [0.0]
In this paper, we consider a class of optimal control problems and introduce an effective solution employing neural networks. To train our multitask neural network, we introduce a novel scheme that dynamically balances the learning across tasks. Through numerical experiments on real-world derivatives pricing problems, we prove that our method outperforms state-of-the-art approaches.
arXiv Detail & Related papers (2024-01-23T17:20:48Z)
Pointer Networks with Q-Learning for Combinatorial Optimization [55.2480439325792]
We introduce the Pointer Q-Network (PQN), a hybrid neural architecture that integrates model-free Q-value policy approximation with Pointer Networks (Ptr-Nets) Our empirical results demonstrate the efficacy of this approach, also testing the model in unstable environments.
arXiv Detail & Related papers (2023-11-05T12:03:58Z)
Optimal Sets and Solution Paths of ReLU Networks [56.40911684005949]
We develop an analytical framework to characterize the set of optimal ReLU networks. We establish conditions for the neuralization of ReLU networks to be continuous, and develop sensitivity results for ReLU networks.
arXiv Detail & Related papers (2023-05-31T18:48:16Z)
Predictive Experience Replay for Continual Visual Control and Forecasting [62.06183102362871]
We present a new continual learning approach for visual dynamics modeling and explore its efficacy in visual control and forecasting. We first propose the mixture world model that learns task-specific dynamics priors with a mixture of Gaussians, and then introduce a new training strategy to overcome catastrophic forgetting. Our model remarkably outperforms the naive combinations of existing continual learning and visual RL algorithms on DeepMind Control and Meta-World benchmarks with continual visual control tasks.
arXiv Detail & Related papers (2023-03-12T05:08:03Z)
Smoothed Online Learning for Prediction in Piecewise Affine Systems [43.64498536409903]
This paper builds on the recently developed smoothed online learning framework. It provides the first algorithms for prediction and simulation in piecewise affine systems.
arXiv Detail & Related papers (2023-01-26T15:54:14Z)
Addressing the issue of stochastic environments and local decision-making in multi-objective reinforcement learning [0.0]
Multi-objective reinforcement learning (MORL) is a relatively new field which builds on conventional Reinforcement Learning (RL) This thesis focuses on what factors influence the frequency with which value-based MORL Q-learning algorithms learn the optimal policy for an environment.
arXiv Detail & Related papers (2022-11-16T04:56:42Z)
Simulating Liquids with Graph Networks [25.013244956897832]
We investigate graph neural networks (GNNs) for learning fluid dynamics. Our results indicate that learning models, such as GNNs, fail to learn the exact underlying dynamics unless the training set is devoid of any other problem-specific correlations.
arXiv Detail & Related papers (2022-03-14T15:39:27Z)
Efficient Model-Based Multi-Agent Mean-Field Reinforcement Learning [89.31889875864599]
We propose an efficient model-based reinforcement learning algorithm for learning in multi-agent systems. Our main theoretical contributions are the first general regret bounds for model-based reinforcement learning for MFC. We provide a practical parametrization of the core optimization problem.
arXiv Detail & Related papers (2021-07-08T18:01:02Z)
Reinforcement Learning for Adaptive Mesh Refinement [63.7867809197671]
We propose a novel formulation of AMR as a Markov decision process and apply deep reinforcement learning to train refinement policies directly from simulation. The model sizes of these policy architectures are independent of the mesh size and hence scale to arbitrarily large and complex simulations.
arXiv Detail & Related papers (2021-03-01T22:55:48Z)
Accurately Solving Physical Systems with Graph Learning [22.100386288615006]
We introduce a novel method to accelerate iterative solvers for physical systems with graph networks. Unlike existing methods that aim to learn physical systems in an end-to-end manner, our approach guarantees long-term stability. Our method improves the run time performance of traditional iterative solvers.
arXiv Detail & Related papers (2020-06-06T15:48:34Z)

This list is automatically generated from the titles and abstracts of the papers in this site.