Difference between revisions of "Apprenticeship Learning - Inverse Reinforcement Learning (IRL)"
m |
m |
||
| Line 18: | Line 18: | ||
* [http://analyticsindiamag.com/guide-to-mbirl-model-based-inverse-reinforcement-learning/ Guide to MBIRL – Model Based Inverse Reinforcement Learning | Aishwarya Verma] | * [http://analyticsindiamag.com/guide-to-mbirl-model-based-inverse-reinforcement-learning/ Guide to MBIRL – Model Based Inverse Reinforcement Learning | Aishwarya Verma] | ||
| − | <img src="https://149695847.v2.pressablecdn.com/wp-content/uploads/2021/02/IRL.png" width=" | + | <img src="https://149695847.v2.pressablecdn.com/wp-content/uploads/2021/02/IRL.png" width="500"> |
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. | ||
Revision as of 08:53, 8 February 2022
YouTube search... ...Google search
- Learning Techniques
- Inside Out - Curious Optimistic Reasoning
- Generative Adversarial Network (GAN)
- Connecting Brains
- 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
- Guide to MBIRL – Model Based Inverse Reinforcement Learning | Aishwarya Verma
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.
