LOGIN TO YOUR ACCOUNT

Username
Password
Remember Me
Or use your Academic/Social account:

CREATE AN ACCOUNT

Or use your Academic/Social account:

Congratulations!

You have just completed your registration at OpenAire.

Before you can login to the site, you will need to activate your account. An e-mail will be sent to you with the proper instructions.

Important!

Please note that this site is currently undergoing Beta testing.
Any new content you create is not guaranteed to be present to the final version of the site upon release.

Thank you for your patience,
OpenAire Dev Team.

Close This Message

CREATE AN ACCOUNT

Name:
Username:
Password:
Verify Password:
E-mail:
Verify E-mail:
*All Fields Are Required.
Please Verify You Are Human:
fbtwitterlinkedinvimeoflicker grey 14rssslideshare1
Leonetti, M; Iocchi, L; Stone, P (2016)
Publisher: Elsevier
Languages: English
Types: Article
Subjects:
Automated planning and reinforcement learning are characterized by complementary views on decision making: the former relies on previous knowledge and computation, while the latter on interaction with the world, and experience. Planning allows robots to carry out different tasks in the same domain, without the need to acquire knowledge about each one of them, but relies strongly on the accuracy of the model. Reinforcement learning, on the other hand, does not require previous knowledge, and allows robots to robustly adapt to the environment, but often necessitates an infeasible amount of experience. We present Domain Approximation for Reinforcement LearnING (DARLING), a method that takes advantage of planning to constrain the behavior of the agent to reasonable choices, and of reinforcement learning to adapt to the environment, and increase the reliability of the decision making process. We demonstrate the effectiveness of the proposed method on a service robot, carrying out a variety of tasks in an office building. We find that when the robot makes decisions by planning alone on a given model it often fails, and when it makes decisions by reinforcement learning alone it often cannot complete its tasks in a reasonable amount of time. When employing DARLING, even when seeded with the same model that was used for planning alone, however, the robot can quickly learn a behavior to carry out all the tasks, improves over time, and adapts to the environment as it changes.
  • The results below are discovered through our pilot algorithms. Let us know how we are doing!

    • [28] Harm V. Seijen and Rich Sutton. True online td(lambda). In Proceedings of the 31st International Conference on Machine Learning (ICML), pages 692-700, 2014.
    • [29] Biplav Srivastava, Tuan Anh Nguyen, Alfonso Gerevini, Subbarao Kambhampati, Minh Binh Do, and Ivan Serina. Domain independent approaches for finding diverse plans. In Proceedings of the Twentieth International Joint Conference on Artificial Intelligence (IJCAI), pages 2016-2022, 2007.
  • No related research data.
  • No similar publications.

Share - Bookmark

Funded by projects

  • NSF | CPS: Breakthrough: Reinforc...
  • NSF | II-NEW: Infrastructure for ...

Cite this article