Joint Optimization of Multi-Objective Reinforcement Learning with Policy Gradient Based Algorithm

• URL: http://arxiv.org/abs/2105.14125v1
• Date: Fri, 28 May 2021 22:20:54 GMT
• Title: Joint Optimization of Multi-Objective Reinforcement Learning with Policy Gradient Based Algorithm
• Authors: Qinbo Bai and Mridul Agarwal and Vaneet Aggarwal
• Abstract summary: We formulate the problem of maximizing a non-linear concave function of multiple long-term objectives. A policy-gradient based model-free algorithm is proposed for the problem. The proposed algorithm is shown to achieve convergence to within an $epsilon$ of the global optima.
• Score: 34.77250498401055
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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 maximizing a non-linear concave function of multiple long-term objectives. A policy-gradient based model-free algorithm is proposed for the problem. To compute an estimate of the gradient, a biased estimator is proposed. The proposed algorithm is shown to achieve convergence to within an $\epsilon$ of the global optima after sampling $\mathcal{O}(\frac{M^4\sigma^2}{(1-\gamma)^8\epsilon^4})$ trajectories where $\gamma$ is the discount factor and $M$ is the number of the agents, thus achieving the same dependence on $\epsilon$ as the policy gradient algorithm for the standard reinforcement learning.

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