Adversarial option-aware hierarchical imitation learning
It has been a challenge to learning skills for an agent from long-horizon unannotated
demonstrations. Existing approaches like Hierarchical Imitation Learning (HIL) are prone to …
demonstrations. Existing approaches like Hierarchical Imitation Learning (HIL) are prone to …
Hierarchical adversarial inverse reinforcement learning
J Chen, T Lan, V Aggarwal - IEEE Transactions on Neural …, 2023 - ieeexplore.ieee.org
Imitation learning (IL) has been proposed to recover the expert policy from demonstrations.
However, it would be difficult to learn a single monolithic policy for highly complex long …
However, it would be difficult to learn a single monolithic policy for highly complex long …
Option-aware adversarial inverse reinforcement learning for robotic control
J Chen, T Lan, V Aggarwal - 2023 IEEE International …, 2023 - ieeexplore.ieee.org
Hierarchical Imitation Learning (HIL) has been proposed to recover highly-complex
behaviors in long-horizon tasks from expert demonstrations by modeling the task hierarchy …
behaviors in long-horizon tasks from expert demonstrations by modeling the task hierarchy …
DiffAIL: Diffusion Adversarial Imitation Learning
Imitation learning aims to solve the problem of defining reward functions in real-world
decision-making tasks. The current popular approach is the Adversarial Imitation Learning …
decision-making tasks. The current popular approach is the Adversarial Imitation Learning …
Rail: Risk-averse imitation learning
Imitation learning algorithms learn viable policies by imitating an expert's behavior when
reward signals are not available. Generative Adversarial Imitation Learning (GAIL) is a state …
reward signals are not available. Generative Adversarial Imitation Learning (GAIL) is a state …
Error bounds of imitating policies and environments for reinforcement learning
In sequential decision-making, imitation learning (IL) trains a policy efficiently by mimicking
expert demonstrations. Various imitation methods were proposed and empirically evaluated …
expert demonstrations. Various imitation methods were proposed and empirically evaluated …
Provably efficient adversarial imitation learning with unknown transitions
Imitation learning (IL) has proven to be an effective method for learning good policies from
expert demonstrations. Adversarial imitation learning (AIL), a subset of IL methods, is …
expert demonstrations. Adversarial imitation learning (AIL), a subset of IL methods, is …
Provable hierarchical imitation learning via EM
Z Zhang, I Paschalidis - International Conference on Artificial …, 2021 - proceedings.mlr.press
Due to recent empirical successes, the options framework for hierarchical reinforcement
learning is gaining increasing popularity. Rather than learning from rewards, we consider …
learning is gaining increasing popularity. Rather than learning from rewards, we consider …
Robust learning from observation with model misspecification
Imitation learning (IL) is a popular paradigm for training policies in robotic systems when
specifying the reward function is difficult. However, despite the success of IL algorithms, they …
specifying the reward function is difficult. However, despite the success of IL algorithms, they …
Directed-info gail: Learning hierarchical policies from unsegmented demonstrations using directed information
The use of imitation learning to learn a single policy for a complex task that has multiple
modes or hierarchical structure can be challenging. In fact, previous work has shown that …
modes or hierarchical structure can be challenging. In fact, previous work has shown that …