Approximate model-assisted Neural Fitted Q-Iteration
By Thomas Lampe, Martin A. Riedmiller. 2014
29 citations recorded by the source corpus.
Subfields: Artificial Intelligence, Reinforcement Learning
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
- Igor Mordatch
- Sergey Levine
- Michael Janner
- Qiyang Li
- Pieter Abbeel
- Chelsea Finn
- Josh Tenenbaum
- Aravind Rajeswaran
In Reinforcement Learning
- Igor Mordatch
- Sergey Levine
- Michael Janner
- Qiyang Li
- Pieter Abbeel
- Chelsea Finn
- Aravind Rajeswaran
- Vikash Kumar
Citations in the corpus
- Offline Reinforcement Learning as One Big Sequence Modeling Problem (2021, methodology): “…most obvious in model-based RL, where it is used to train predictive models that can then be used for planning or policy learning (Sutton, 1990; Silver et al., 2008; Fairbank, 2008; Deisenroth & Rasmussen, 2011; Lampe & Riedmiller, 2014; Heess et al., 2015b; Janner et al., 2020; Amos et al., 2021).”
- γ-Models: Generative Temporal Difference Learning for Infinite-Horizon Prediction (2020, methodology): “…et al., 2014; Nagabandi et al., 2018), feeding model-generated data into an otherwise model-free optimizer (Sutton, 1990; Silver et al., 2008; Lampe & Riedmiller, 2014; Kalweit & Boedecker, 2017; Luo et al., 2019), using model predictions to improve the quality of target values for temporal…”
- Reinforcement Learning as One Big Sequence Modeling Problem (2021, background): “The most obvious is model-based RL, where it is used to train predictive models that can then be used for planning or policy learning [58, 53, 14, 13, 36, 22, 10, 63, 1].”
- Uncertainty-driven Imagination for Continuous Deep Reinforcement Learning (2017, unknown): “Lampe and Riedmiller [7] extended Neural-fitted Q-Iteration (NFQ) [8] by a neural model and achieved significantly faster learning in terms of required interactions on the Cartpole Swing up environment.”