PA2D-MORL: Pareto Ascent Directional Decomposition based Multi-Objective Reinforcement Learning
arXiv:2603.19579v1 Announce Type: new Abstract: Multi-objective reinforcement learning (MORL) provides an effective solution for decision-making problems involving conflicting objectives. However, achieving high-quality approximations to the Pareto policy set remains challenging, especially in complex tasks with continuous or high-dimensional state-action space. In this paper, we propose the Pareto Ascent Directional Decomposition based Multi-Objective Reinforcement Learning (PA2D-MORL) method, which constructs an efficient scheme for multi-objective problem decomposition and policy improvement, leading to a superior approximation of Pareto policy set. The proposed method leverages Pareto ascent direction to select the scalarization weights and computes the multi-objective policy gradient, which determines the policy optimization direction and ensures joint improvement on all objectives. Meanwhile, multiple policies are selectively optimized under an evolutionary framework to approxim
arXiv:2603.19579v1 Announce Type: new Abstract: Multi-objective reinforcement learning (MORL) provides an effective solution for decision-making problems involving conflicting objectives. However, achieving high-quality approximations to the Pareto policy set remains challenging, especially in complex tasks with continuous or high-dimensional state-action space. In this paper, we propose the Pareto Ascent Directional Decomposition based Multi-Objective Reinforcement Learning (PA2D-MORL) method, which constructs an efficient scheme for multi-objective problem decomposition and policy improvement, leading to a superior approximation of Pareto policy set. The proposed method leverages Pareto ascent direction to select the scalarization weights and computes the multi-objective policy gradient, which determines the policy optimization direction and ensures joint improvement on all objectives. Meanwhile, multiple policies are selectively optimized under an evolutionary framework to approximate the Pareto frontier from different directions. Additionally, a Pareto adaptive fine-tuning approach is applied to enhance the density and spread of the Pareto frontier approximation. Experiments on various multi-objective robot control tasks show that the proposed method clearly outperforms the current state-of-the-art algorithm in terms of both quality and stability of the outcomes.
Executive Summary
The article introduces PA2D-MORL, a novel multi-objective reinforcement learning method that leverages Pareto ascent direction to decompose complex tasks and approximate the Pareto policy set. The approach combines policy gradient optimization with evolutionary multi-objective optimization to achieve high-quality approximations. Experiments demonstrate the superiority of PA2D-MORL in robot control tasks. The method's efficiency and ability to improve joint objectives make it a promising solution for decision-making problems with multiple conflicting objectives.
Key Points
- ▸ PA2D-MORL integrates Pareto ascent direction with policy gradient optimization
- ▸ The method employs evolutionary multi-objective optimization for efficient policy improvement
- ▸ Experiments showcase the superiority of PA2D-MORL in multi-objective robot control tasks
Merits
Strength in Policy Optimization
PA2D-MORL effectively optimizes policies for multiple objectives, ensuring joint improvement in conflicting tasks.
Adaptive Fine-Tuning Approach
The Pareto adaptive fine-tuning feature enhances the density and spread of the Pareto frontier approximation.
Improved Efficiency
The method's decomposition scheme allows for efficient exploration of the Pareto policy set.
Demerits
Computational Complexity
The evolutionary multi-objective optimization process may introduce computational complexity, particularly in high-dimensional state-action spaces.
Expert Commentary
The PA2D-MORL method is a significant contribution to the field of multi-objective reinforcement learning. By leveraging Pareto ascent direction and adaptive fine-tuning, the approach effectively addresses the challenges of approximating the Pareto policy set. While the method's efficiency and ability to improve joint objectives are notable strengths, the potential computational complexity in high-dimensional spaces is a limitation that warrants further exploration. As the field continues to evolve, PA2D-MORL is likely to have a lasting impact on the development of decision-making algorithms for complex, multi-objective tasks.
Recommendations
- ✓ Future research should focus on scaling PA2D-MORL to larger, more complex tasks and exploring its application in diverse domains.
- ✓ The method's adaptation to different problem types, such as discrete or mixed-integer state-action spaces, would further enhance its practicality and applicability.
Sources
Original: arXiv - cs.AI
