A survey of inverse reinforcement learning techniques

By Zhifei Shao, Meng Joo Er. 2012

116 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Control and Systems Engineering

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Citations in the corpus

Reinforcement Learning in Healthcare: A Survey (2019, methodology): “presumably optimal trajectories so that the reward is consistent with the observed behaviors. The problem of deriving a reward function from observed behavior is referred to as Inverse RL (IRL) [53], [54], which has received an increasingly high interest by researchers in the past few years. Numerou”
AirSim: High-Fidelity Visual and Physical Simulation for Autonomous Vehicles (2017, background): “Recently, paradigms such as reinforcement learning [18], learning-by-demonstration [14, 9] and transfer learning [23] are proving a natural means to train various robotics systems.”
Inverse Reinforcement Learning in Swarm Systems (2016, background): “As such, they are assumed to be interchangeable.”
Extrapolating Beyond Suboptimal Demonstrations via Inverse Reinforcement Learning from Observations (2019, background): “Rather than directly learn to mimic the demonstrator, inverse reinforcement learning (IRL) (Gao et al., 2012; Arora & Doshi, 2018) seeks to find a reward function that models the intention of the demonstrator, thereby allowing generalization to states that were unvisited during demonstration.”
PixL2R: Guiding Reinforcement Learning Using Natural Language by Mapping Pixels to Rewards (2020, background): “To get around the challenge of reward design, learning from demonstrations is a popular approach [1, 2]; however, providing demonstrations to robots requires teleoperating or kinesthetic teaching, which is difficult and time-consuming to provide, particularly for non-experts.”
Learning strategies in table tennis using inverse reinforcement learning (2014, background): “A recent review of IRL algorithms can be found in (Zhifei and Joo, 2012).”
Learning manipulation skills from a single demonstration (2018, methodology): “For a broader overview on IOC approaches we refer the reader to the survey paper of Zhifei and Joo (2012) and for an overview on imitation learning in robotics we recommend (Argall et al. 2009).”
Machine Teaching for Inverse Reinforcement Learning: Algorithms and Applications (2018, methodology): “This property is true of all standard IRL methods (Gao et al. 2012; Arora and Doshi 2018).”