A multi-objective evolutionary approach to Pareto-optimal model trees

Czajkowski, Marcin; Krȩtowski, Marek

doi:10.1007/s00500-018-3646-3

Cited by 15 publications

(5 citation statements)

References 39 publications

(42 reference statements)

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“…Other works [15] (and [18], [20]- [22], even if they are not applied to RL tasks) induce trees using evolutionary approaches. However, these approaches only rely on an EA.…”

Section: Methodsmentioning

confidence: 99%

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Evolutionary Learning of Interpretable Decision Trees

Custode

Iacca

2023

IEEE Access

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In the last decade, reinforcement learning (RL) has been used to solve several tasks with human-level performance. However, there is a growing demand for interpretable RL, i.e., there is the need to understand how a RL agent works and the rationale of its decisions. Not only do we need interpretability to assess the safety of such agents, but also we may need it to gain insights into unknown problems. In this work, we propose a novel optimization approach to interpretable RL that builds decision trees. While techniques that optimize decision trees for RL do exist, they usually employ greedy algorithms or do not exploit the rewards given by the environment. For these reasons, these techniques may either get stuck in local optima or be inefficient. On the contrary, our approach is based on a two-level optimization scheme that combines the advantages of evolutionary algorithms with the benefits of Q-learning. This method allows decomposing the problem into two sub-problems: the problem of finding a meaningful decomposition of the state space, and the problem of associating an action to each subspace. We test the proposed method on three well-known RL benchmarks, as well as on a pandemic control task, on which it results competitive with the state-of-the-art in both performance and interpretability.

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“…Other works [15] (and [18], [20]- [22], even if they are not applied to RL tasks) induce trees using evolutionary approaches. However, these approaches only rely on an EA.…”

Section: Methodsmentioning

confidence: 99%

“…In [20], the authors propose a multi-objective EA to evolve regression trees and model trees. They find a Pareto front for RMSE, the number of nodes, and the number of attributes.…”

Section: Related Workmentioning

confidence: 99%

Evolutionary Learning of Interpretable Decision Trees

Custode

Iacca

2023

IEEE Access

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“…Other works [11] (and [12,13,14,15], even if they are not applied to reinforcement learning tasks) induce trees by means of evolutionary approaches.…”

Section: Methodsmentioning

confidence: 99%

“…In [13], the authors propose a multi-objective EA to evolve regression trees and model trees. They use a Pareto front to optimize RMSE, number of nodes, number of attributes.…”

Section: Theorymentioning

confidence: 99%

Evolutionary learning of interpretable decision trees

Custode,

Iacca

2020

Preprint

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Reinforcement learning techniques achieved human-level performance in several tasks in the last decade. However, in recent years, the need for interpretability emerged: we want to be able to understand how a system works and the reasons behind its decisions. Not only we need interpretability to assess the safety of the produced systems, we also need it to extract knowledge about unknown problems. While some techniques that optimize decision trees for reinforcement learning do exist, they usually employ greedy algorithms or they do not exploit the rewards given by the environment. This means that these techniques may easily get stuck in local optima. In this work, we propose a novel approach to interpretable reinforcement learning that uses decision trees. We present a two-level optimization scheme that combines the advantages of evolutionary algorithms with the advantages of Q-learning. This way we decompose the problem into two sub-problems: the problem of finding a meaningful and useful decomposition of the state space, and the problem of associating an action to each state. We test the proposed method on three well-known reinforcement learning benchmarks, on which it results competitive with respect to the stateof-the-art in both performance and interpretability. Finally, we perform an ablation study that confirms that using the two-level optimization scheme gives a boost in performance in non-trivial environments with respect to a one-layer optimization technique.

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“…The Review of Literature will discuss the related work of the study. Czajkowski and Kretowski, [25] discuss the multi-objective design approach to model-tree recognition. The model tree is a specific instance of a tree of preference built to take care of relapse problems.…”

Section: Literature Reviewmentioning

confidence: 99%

A Review of Multi-objective Evolutionary Algorithms for Information Retrieval System

2020

IJETER

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Information retrieval systems (IRSs) presentation is generally calculated using two special principles, accuracy and analysis. Exactness is the proportion of appropriate documents retrieved by the Information Retrieval System to the overall number of archives retrieved in the context of a client's inquiry; while review is the proportion of the quantity of pertinent reports recovered to the complete number of important reports for the client's question that are present in the narrative database. We present an investigation of two notable broadly useful multi-objective transformative algorithms, Challenges of Multi-objective Evolutionary Algorithms, Applications and Recent improvements in Multi-objective Evolutionary Algorithms all briefly. Further this examination talks about the diverse writing attempts to make the examination.

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A multi-objective evolutionary approach to Pareto-optimal model trees

Cited by 15 publications

References 39 publications

Evolutionary Learning of Interpretable Decision Trees

Evolutionary Learning of Interpretable Decision Trees

Evolutionary learning of interpretable decision trees

A Review of Multi-objective Evolutionary Algorithms for Information Retrieval System

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