RL-Glue

RL-Glue: Language-Independent Software for Reinforcement-Learning Experiments. RL-Glue is a standard, language-independent software package for reinforcement-learning experiments. The standardization provided by RL-Glue facilitates code sharing and collaboration. Code sharing reduces the need to re-engineer tasks and experimental apparatus, both common barriers to comparatively evaluating new ideas in the context of the literature. Our software features a minimalist interface and works with several languages and computing platforms. RL-Glue compatibility can be extended to any programming language that supports network socket communication. RL-Glue has been used to teach classes, to run international competitions, and is currently used by several other open-source software and hardware projects.

This software is also peer reviewed by journal TOMS.


References in zbMATH (referenced in 8 articles )

Showing results 1 to 8 of 8.
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  1. Bianchi, Reinaldo A. C.; Celiberto, Luiz A. jun.; Santos, Paulo E.; Matsuura, Jackson P.; Lopez de Mantaras, Ramon: Transferring knowledge as heuristics in reinforcement learning: a case-based approach (2015)
  2. Geramifard, Alborz; Dann, Christoph; Klein, Robert H.; Dabney, William; How, Jonathan P.: RLPy: a value-function-based reinforcement learning framework for education and research (2015) ioport
  3. Hester, Todd: TEXPLORE: temporal difference reinforcement learning for robots and time-constrained domains (2013)
  4. Hester, Todd; Stone, Peter: Real-time sample-efficient reinforcement learning for robots (2013) ioport
  5. Hafner, Roland; Riedmiller, Martin: Reinforcement learning in feedback control: challenges and benchmarks from technical process control (2011) ioport
  6. Kovacs, Tim; Egginton, Robert: On the analysis and design of software for reinforcement learning, with a survey of existing systems (2011) ioport
  7. Vamplew, Peter; Dazeley, Richard; Berry, Adam; Issabekov, Rustam; Dekker, Evan: Empirical evaluation methods for multiobjective reinforcement learning algorithms (2011) ioport
  8. Tanner, Brian; White, Adam: RL-glue: language-independent software for reinforcement-learning experiments (Machine learning open source software paper) (2009) ioport