Construction of approximation spaces for reinforcement learning
By Wendelin Böhmer, Steffen Grünewälder, Yun Shen, Marek Musial, Klaus Obermayer. 2013
23 citations recorded by the source corpus.
Subfields: Reinforcement Learning, Representation & Self-Supervised Learning
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Reinforcement Learning
- Sergey Levine
- Pieter Abbeel
- Chelsea Finn
- Shixiang Shane Gu
- Trevor Darrell
- Abhishek Gupta
- Shimon Whiteson
- Jan Peters
In Representation & Self-Supervised Learning
- Sergey Levine
- Chelsea Finn
- Pieter Abbeel
- Trevor Darrell
- Jost Tobias Springenberg
- J. Urgen Schmidhuber
- Jan Peters
- Han-Jia Ye
Citations in the corpus
- End-to-End Training of Deep Visuomotor Policies (2015, background): “, 2015), and unsupervised state-space learning (Böhmer et al., 2013; Jonschkowski and Brock, 2014).”
- Value-Aware Loss Function for Model-based Reinforcement Learning (2017, background): “…(e.g., [Silver et al., 2007; Liang et al., 2016]) or automatically generated data-dependently, e.g., [Petrik, 2007; Mahadevan and Maggioni, 2007; Parr et al., 2007; Mahadevan and Liu, 2010; Geramifard et al., 2011; Farahmand and Precup, 2012; B¨ohmer et al., 2013; Milani Fard et al., 2013].”
- Non-parametric Policy Search with Limited Information Loss (2017, methodology): “This hand-tuning can largely be avoided by learning a suitable representation (Jonschkowski and Brock, 2015; Böhmer et al., 2013).”
- Autonomous Learning of State Representations for Control: An Emerging Field Aims to Autonomously Learn State Representations for Reinforcement Learning Agents from Their Real-World Sensor Observations (2015, methodology): “A training set uniformly sampled in S (not in Z) would probably yield the most general representations [5].”