Difference between revisions of "Apprenticeship Learning - Inverse Reinforcement Learning (IRL)"
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* [[Imitation Learning]] | * [[Imitation Learning]] | ||
| − | * [[Reinforcement Learning]] | + | * [[Learning Techniques]] |
| + | ** [[Reinforcement Learning]] | ||
* [[Inside Out - Curious Optimistic Reasoning]] | * [[Inside Out - Curious Optimistic Reasoning]] | ||
* [[Generative Adversarial Network (GAN)]] | * [[Generative Adversarial Network (GAN)]] | ||
Revision as of 15:18, 8 December 2019
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- Imitation Learning
- Learning Techniques
- Inside Out - Curious Optimistic Reasoning
- Generative Adversarial Network (GAN)
- A Survey of Inverse Reinforcement Learning: Challenges, Methods and Progress | Saurabh Arora, Prashant Doshi 18 Jun 2018
- Machine Teaching for Inverse Reinforcement Learning: Algorithms and Applications | Daniel S. Brown, Scott Niekum 23 Jun 2018
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.
