The Reinforcement Learning Competition 2014

By Christos Dimitrakakis, Guangliang Li, Nikolaos Tziortziotis. 2014

15 citations recorded by the source corpus.

Reinforcement learning is one of the most general problems in artificial intelligence. It has been used to model problems in automated experiment design, control, economics, game playing, scheduling and telecommunications. The aim of the reinforcement learning competition is to encourage the development of very general learning agents for arbitrary reinforcement learning problems and to provide a test-bed for the unbiased evaluation of algorithms.

Subfields: Economics and Econometrics, Reinforcement Learning

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In Reinforcement Learning

Citations in the corpus

OpenAI Gym (2016, background): “…released, such as the Arcade Learning Environment (ALE) [5], which exposed a collection of Atari 2600 games as reinforcement learning problems, and recently the RLLab benchmark for continuous control [6], to which we refer the reader for a survey on other RL benchmarks, including [7, 8, 9, 10, 11].”
Benchmarking Deep Reinforcement Learning for Continuous Control (2016, background): “A series of RL competitions has also been held in recent years (Dutech et al., 2005; Dimitrakakis et al., 2014), again with relatively low-dimensional actions.”
A Shared Task on Bandit Learning for Machine Translation (2017, methodology): “This metric has been used in reinforcement learning competitions (Dimitrakakis et al., 2014).”
Evaluation in artificial intelligence: from task-oriented to ability-oriented measurement (2017, background): “In 2014, three domains were included (helicopter, polyathlon and invasive species) (Dimitrakakis et al, 2014), although in the past there have been many other domains.”