Reinforcement learning (RL) algorithms often require a significant number of experiences to learn a policy capable of achieving desired goals in multi-goal robot manipulation tasks with sparse rewards. Hindsight Experience Replay (HER) is an existing method that improves learning efficiency by using failed trajectories and replacing the original goals with hindsight goals that are uniformly sampled from the visited states. However, HER has a limitation: the hindsight goals are mostly near the initial state, which hinders solving tasks efficiently if the desired goals are far from the initial state. To overcome this limitation, we introduce a curriculum learning method called HERDT (HER with Decision Trees). HERDT uses binary DTs to generate curriculum goals that guide a robotic agent progressively from an initial state toward a desired goal. During the warm-up stage, DTs are optimized using the Grammatical Evolution algorithm. In the training stage, curriculum goals are then sampled by DTs to help the agent navigate the environment. Since binary DTs generate discrete values, we fine-tune these curriculum points by incorporating a feedback value (i.e., the Q-value). This fine-tuning enables us to adjust the difficulty level of the generated curriculum points, ensuring that they are neither overly simplistic nor excessively challenging. In other words, these points are precisely tailored to match the robot’s ongoing learning policy. We evaluate our proposed approach on different sparse reward robotic manipulation tasks and compare it with the state-of-the-art HER approach. Our results demonstrate that our method consistently outperforms or matches the existing approach in all the tested tasks.

Hindsight Experience Replay with Evolutionary Decision Trees for Curriculum Goal Generation / Sayar, Erdi; Vintaykin, Vladislav; Iacca, Giovanni; Knoll, Alois. - 14635:(2024), pp. 3-18. (Intervento presentato al convegno 27th European Conference on Applications of Evolutionary Computation, EvoApplications 2024 held as part of EvoStar 2024 tenutosi a Aberystwyth nel 3rd-5th April 2024) [10.1007/978-3-031-56855-8_1].

Hindsight Experience Replay with Evolutionary Decision Trees for Curriculum Goal Generation

Iacca, Giovanni;
2024-01-01

Abstract

Reinforcement learning (RL) algorithms often require a significant number of experiences to learn a policy capable of achieving desired goals in multi-goal robot manipulation tasks with sparse rewards. Hindsight Experience Replay (HER) is an existing method that improves learning efficiency by using failed trajectories and replacing the original goals with hindsight goals that are uniformly sampled from the visited states. However, HER has a limitation: the hindsight goals are mostly near the initial state, which hinders solving tasks efficiently if the desired goals are far from the initial state. To overcome this limitation, we introduce a curriculum learning method called HERDT (HER with Decision Trees). HERDT uses binary DTs to generate curriculum goals that guide a robotic agent progressively from an initial state toward a desired goal. During the warm-up stage, DTs are optimized using the Grammatical Evolution algorithm. In the training stage, curriculum goals are then sampled by DTs to help the agent navigate the environment. Since binary DTs generate discrete values, we fine-tune these curriculum points by incorporating a feedback value (i.e., the Q-value). This fine-tuning enables us to adjust the difficulty level of the generated curriculum points, ensuring that they are neither overly simplistic nor excessively challenging. In other words, these points are precisely tailored to match the robot’s ongoing learning policy. We evaluate our proposed approach on different sparse reward robotic manipulation tasks and compare it with the state-of-the-art HER approach. Our results demonstrate that our method consistently outperforms or matches the existing approach in all the tested tasks.
2024
Applications of Evolutionary Computation. EvoApplications 2024
Cham, Svizzera
Springer Science and Business Media Deutschland GmbH
9783031568541
9783031568558
Sayar, Erdi; Vintaykin, Vladislav; Iacca, Giovanni; Knoll, Alois
Hindsight Experience Replay with Evolutionary Decision Trees for Curriculum Goal Generation / Sayar, Erdi; Vintaykin, Vladislav; Iacca, Giovanni; Knoll, Alois. - 14635:(2024), pp. 3-18. (Intervento presentato al convegno 27th European Conference on Applications of Evolutionary Computation, EvoApplications 2024 held as part of EvoStar 2024 tenutosi a Aberystwyth nel 3rd-5th April 2024) [10.1007/978-3-031-56855-8_1].
File in questo prodotto:
File Dimensione Formato  
Hindsight Experience Replay with Evolutionary Decision Trees for Curriculum Goal Generation.pdf

Solo gestori archivio

Tipologia: Versione editoriale (Publisher’s layout)
Licenza: Tutti i diritti riservati (All rights reserved)
Dimensione 3.87 MB
Formato Adobe PDF
3.87 MB Adobe PDF   Visualizza/Apri
sayar.pdf

embargo fino al 21/03/2025

Tipologia: Post-print referato (Refereed author’s manuscript)
Licenza: Tutti i diritti riservati (All rights reserved)
Dimensione 5.16 MB
Formato Adobe PDF
5.16 MB Adobe PDF   Visualizza/Apri

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11572/405931
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 0
  • ???jsp.display-item.citation.isi??? 0
  • OpenAlex ND
social impact