Joint optimization of concave scalarized multi-objective reinforcement learning with policy gradient based algorithm
Many engineering problems have multiple objectives, and the overall aim is to optimize a
non-linear function of these objectives. In this paper, we formulate the problem of …
non-linear function of these objectives. In this paper, we formulate the problem of …
Joint optimization of multi-objective reinforcement learning with policy gradient based algorithm
Many engineering problems have multiple objectives, and the overall aim is to optimize a
non-linear function of these objectives. In this paper, we formulate the problem of …
non-linear function of these objectives. In this paper, we formulate the problem of …
Sample Complexity of Incremental Policy Gradient Methods for Solving Multi-Task Reinforcement Learning
Y Bai - 2024 - vtechworks.lib.vt.edu
We consider a multi-task learning problem, where an agent is presented a number of N
reinforcement learning tasks. To solve this problem, we are interested in studying the …
reinforcement learning tasks. To solve this problem, we are interested in studying the …
Finite-time complexity of incremental policy gradient methods for solving multi-task reinforcement learning
Y Bai, T Doan - 6th Annual Learning for Dynamics & Control …, 2024 - proceedings.mlr.press
We consider a multi-task learning problem, where an agent is presented a number of $ N $
reinforcement learning tasks. To solve this problem, we are interested in studying the …
reinforcement learning tasks. To solve this problem, we are interested in studying the …
A Scale-Independent Multi-Objective Reinforcement Learning with Convergence Analysis
M Amidzadeh - 2023 62nd IEEE Conference on Decision and …, 2023 - ieeexplore.ieee.org
Many sequential decision-making problems need optimization of different objectives which
possibly conflict with each other. The conventional way to deal with a multitask problem is to …
possibly conflict with each other. The conventional way to deal with a multitask problem is to …
A hybrid stochastic policy gradient algorithm for reinforcement learning
We propose a novel hybrid stochastic policy gradient estimator by combining an unbiased
policy gradient estimator, the REINFORCE estimator, with another biased one, an adapted …
policy gradient estimator, the REINFORCE estimator, with another biased one, an adapted …
Multi-objective reinforcement learning method for acquiring all Pareto optimal policies simultaneously
Y Mukai, Y Kuroe, H Iima - 2012 IEEE international conference …, 2012 - ieeexplore.ieee.org
This paper studies multi-objective reinforcement learning problems in which an agent gains
multiple rewards. In ordinary multi-objective reinforcement learning methods, only a single …
multiple rewards. In ordinary multi-objective reinforcement learning methods, only a single …
Solving the scalarization issues of Advantage-based Reinforcement Learning algorithms
In this research, some of the issues that arise from the scalarization of the multi-objective
optimization problem in the Advantage Actor–Critic (A2C) reinforcement learning algorithm …
optimization problem in the Advantage Actor–Critic (A2C) reinforcement learning algorithm …
Multi-agent reinforcement learning for training and non-linear optimization
A Morcos, A West, B Maguire - Artificial Intelligence and …, 2022 - spiedigitallibrary.org
The field of Reinforcement Learning continues to show promise in solving old problems in
new and innovative ways. Thanks to the algorithms' ability to learn without an explicit set of …
new and innovative ways. Thanks to the algorithms' ability to learn without an explicit set of …
[PDF][PDF] Adaptive objective selection for correlated objectives in multi-objective reinforcement learning
In this paper we introduce a novel scale-invariant and parameterless technique, called
adaptive objective selection, that allows a temporal-difference learning agent to exploit the …
adaptive objective selection, that allows a temporal-difference learning agent to exploit the …