Efficient Search for Optimal Trade-offs in Multi-objective Reinforcement Learning

Alapfogalmak

An efficient method for searching the space of linear utility functions to approximate the Pareto front in multi-objective reinforcement learning problems.

Kivonat

The content describes a method for efficiently solving multi-objective reinforcement learning (MORL) problems by decomposing the problem into a set of scalar reinforcement learning sub-problems. The key aspects of the proposed approach, named UCB-MOPPO, are:

1. Decomposition of the MORL problem into scalar RL sub-problems:

   • The overall scalarisation weight simplex is decomposed into K sub-spaces.
   • A separate policy is trained for each sub-problem by conditioning it on scalarisation vectors sampled from the associated sub-space.
   • This two-layer decomposition allows different policies to specialise in different sub-spaces of the scalarisation vector space.

2. Scalarisation-vector-conditioned Actor-Critic:

   • Both the policy network and the value network are conditioned on the scalarisation vector.
   • This allows a single policy to express different trade-offs between objectives by generalising to a neighbourhood of scalarisation vectors.

3. Surrogate-assisted maximisation of CCS hypervolume:

   • An acquisition function based on Upper Confidence Bound (UCB) is used to select the scalarisation vectors to train on from each sub-space.
   • At each stage of the training process, the selected scalarisation vectors are those expected to maximise the hypervolume of the resulting Convex Coverage Set (CCS) the most.

The proposed UCB-MOPPO method is shown to outperform various MORL baselines on MuJoCo benchmark problems across different random seeds. It achieves significantly higher hypervolume than the PGMORL baseline, while requiring fewer policies to be maintained, making it suitable for resource-constrained environments.

Statisztikák

The content does not provide any specific numerical data or metrics. It focuses on describing the proposed UCB-MOPPO method and comparing its performance to baseline methods on MuJoCo benchmark problems.

Idézetek

The content does not contain any direct quotes that are relevant to the key logics.

Főbb Kivonatok

UCB-driven Utility Function Search for Multi-objective Reinforcement Learning

by Yucheng Shi,... : arxiv.org 05-02-2024

https://arxiv.org/pdf/2405.00410.pdf

Mélyebb kérdések

How can the proposed UCB-MOPPO method be extended to handle non-linear utility functions in multi-objective reinforcement learning

The proposed UCB-MOPPO method can be extended to handle non-linear utility functions in multi-objective reinforcement learning by adapting the surrogate-assisted optimization framework. Instead of using linear models for the surrogate model, more complex non-linear models can be employed to capture the relationships between the scalarisation vectors and the objective values. This would involve training neural networks or other non-linear models to predict the expected changes in objective values based on the scalarisation vectors. By incorporating non-linear models into the surrogate-assisted optimization process, the method can effectively handle non-linear utility functions and provide more accurate predictions for optimizing the Pareto front.

What are the potential limitations or drawbacks of the two-layer decomposition approach, and how could it be further improved

The two-layer decomposition approach in MORL may have some limitations and drawbacks that could be addressed for further improvement. One potential limitation is the scalability of the method as the number of objectives or sub-spaces increases. Managing a large number of policies and scalarisation vectors can become computationally intensive and may lead to slower convergence or higher memory requirements. To improve this, more efficient algorithms for policy management and selection of scalarisation vectors could be developed. Additionally, the method may struggle with highly complex or non-convex Pareto fronts, where the linear decomposition may not capture the full complexity of the problem. Introducing more sophisticated decomposition techniques or adaptive approaches to adjust the decomposition based on the problem characteristics could enhance the method's performance in such scenarios.

Can the surrogate-assisted optimization framework be applied to other Pareto front quality metrics beyond hypervolume, and how would that affect the performance and characteristics of the resulting Pareto fronts

The surrogate-assisted optimization framework can be applied to other Pareto front quality metrics beyond hypervolume to further enhance the performance and characteristics of the resulting Pareto fronts. For example, metrics such as generational distance, inverted generational distance, epsilon indicator, or spacing could be incorporated into the framework to provide a more comprehensive evaluation of the Pareto front quality. By optimizing multiple quality metrics simultaneously, the method can generate Pareto fronts that exhibit a better balance between convergence and diversity, leading to more diverse and well-distributed solutions. This approach would provide decision-makers with a richer set of options to choose from, catering to a wider range of preferences and requirements.