Difference between revisions of "Apprenticeship Learning - Inverse Reinforcement Learning (IRL)"

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* [http://arxiv.org/pdf/1806.06877.pdf A Survey of Inverse Reinforcement Learning: Challenges, Methods and Progress | Saurabh Arora, Prashant Doshi] 18 Jun 2018
 
* [http://arxiv.org/pdf/1806.06877.pdf A Survey of Inverse Reinforcement Learning: Challenges, Methods and Progress | Saurabh Arora, Prashant Doshi] 18 Jun 2018
 
* [http://arxiv.org/pdf/1805.07687.pdf Machine Teaching for Inverse Reinforcement Learning: Algorithms and Applications | Daniel S. Brown, Scott Niekum] 23 Jun 2018
 
* [http://arxiv.org/pdf/1805.07687.pdf Machine Teaching for Inverse Reinforcement Learning: Algorithms and Applications | Daniel S. Brown, Scott Niekum] 23 Jun 2018
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* [http://analyticsindiamag.com/guide-to-mbirl-model-based-inverse-reinforcement-learning/ Guide to MBIRL – Model Based Inverse Reinforcement Learning | Aishwarya Verma]
  
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<img src="https://149695847.v2.pressablecdn.com/wp-content/uploads/2021/02/IRL.png" width="800">
  
 
Inverse reinforcement learning (IRL) infers/derives a reward function from observed behavior/demonstrations, allowing for policy improvement and generalization. While ordinary "reinforcement learning" involves using rewards and punishments to learn behavior, in IRL the direction is reversed, and a robot observes a person's behavior to figure out what goal that behavior seems to be trying to achieve.   
 
Inverse reinforcement learning (IRL) infers/derives a reward function from observed behavior/demonstrations, allowing for policy improvement and generalization. While ordinary "reinforcement learning" involves using rewards and punishments to learn behavior, in IRL the direction is reversed, and a robot observes a person's behavior to figure out what goal that behavior seems to be trying to achieve.   
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<youtube>0q30_gDlrwk</youtube>
 
<youtube>0q30_gDlrwk</youtube>

Revision as of 08:53, 8 February 2022

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Inverse reinforcement learning (IRL) infers/derives a reward function from observed behavior/demonstrations, allowing for policy improvement and generalization. While ordinary "reinforcement learning" involves using rewards and punishments to learn behavior, in IRL the direction is reversed, and a robot observes a person's behavior to figure out what goal that behavior seems to be trying to achieve.