Inverse Reinforcement Learning Explained
Автор: Edan Meyer
Загружено: 2021-05-30
Просмотров: 13511
Описание:
Inverse Reinforcement Learning (Inverse RL / IRL) is a type of RL where the objective is opposite from forward RL. Instead of learning a policy from a reward function, we are trying to learn a reward function from a policy or demonstration of a task. In this video I go through why to use Inverse Reinforcement Learning, why to use Inverse RL, examples of IRL, some of the theory, and some existing IRL methods. I cover one of the original papers by Andrew Ng, as well as some newer works on Maximum Entropy IRL (MaxEnt IRL), and Adversarial IRL.
RL Theory playlist: • Intro to Reinforcement Learning Made Simple
IRL Algorithms paper: https://ai.stanford.edu/~ang/papers/i...
MaxEnt IRL paper: https://www.aaai.org/Papers/AAAI/2008...
Adversarial IRL paper: https://arxiv.org/abs/1710.11248
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