Inverse Reinforcement Learning
By Pieter Abbeel, Andrew Y. Ng. 2010
99 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
- Li Fei-Fei
- Sergey Levine
- Silvio Savarese
- Yuke Zhu
- Dieter Fox
- Chelsea Finn
- Ajay Mandlekar
- Trevor Darrell
In Reinforcement Learning
- Silvio Savarese
- Li Fei-Fei
- Yuke Zhu
- Dieter Fox
- Ajay Mandlekar
- Sergey Levine
- Jonathan Booher
- Animesh Garg
Citations in the corpus
- IRIS: Implicit Reinforcement without Interaction at Scale for Learning Control from Offline Robot Manipulation Data (2019, background): “Imitation learning methods are either offline, such as Behavioral Cloning [30, 34, 35], or online, such as Inverse Reinforcement Learning (IRL) [1, 20].”
- ROBOTURK: A Crowdsourcing Platform for Robotic Skill Learning through Imitation (2018, background): “Imitation Learning is commonly posed either as inverse reinforcement learning [12, 13] or behavioral cloning [14, 15], both of which require demonstrations.”
- Continuous State-Space Models for Optimal Sepsis Treatment: a Deep Reinforcement Learning Approach (2017, background): “Another approach could be to use inverse RL techniques (Abbeel and Ng, 2010) to derive a suitable reward function based on the actions of experts (the physicians).”
- RAIL: Risk-Averse Imitation Learning (2017, methodology): “The second category of algorithms, known as Inverse Reinforcement Learning (IRL) Russell, Ng, Russell, and others, Abbeel and Ng (1998, 2000, 2011), attempt to uncover the expert’s reward function from the demonstrated trajectories.”
- Automated Planning and Acting (2016, background): “This difficulty leads to the inverse reinforcement learning problem [3].”
- How Generative Adversarial Networks and Its Variants Work : An Overview of GAN (2017, background): “Inverse reinforcement learning (IRL) [1] is similar reinforcement learning in that its objective is to find the optimal policy.”
- Deliberation for autonomous robots: A survey (2017, background): “Inverse reinforcement learning aims to acquire reward functions from demonstrated behavior [163, 1].”