Multi-task hierarchical adversarial inverse reinforcement learning
Abstract Multi-task Imitation Learning (MIL) aims to train a policy capable of performing a
distribution of tasks based on multi-task expert demonstrations, which is essential for …
distribution of tasks based on multi-task expert demonstrations, which is essential for …
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 …
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 …
Multi-task reinforcement learning with attention-based mixture of experts
Multi-task learning is an important problem in reinforcement learning. Training multiple tasks
together brings benefits from the shared useful information across different tasks and often …
together brings benefits from the shared useful information across different tasks and often …
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 …
Task-oriented deep reinforcement learning for robotic skill acquisition and control
G Xiang, J Su - IEEE transactions on cybernetics, 2019 - ieeexplore.ieee.org
Reinforcement learning (RL) and imitation learning (IL), especially equipped with deep
neural networks, have been widely studied for autonomous robotic skill acquisition and …
neural networks, have been widely studied for autonomous robotic skill acquisition and …
Seek for commonalities: Shared features extraction for multi-task reinforcement learning via adversarial training
J Meng, F Zhu - Expert Systems with Applications, 2023 - Elsevier
Multi-task reinforcement learning is promising to alleviate the low sample efficiency and high
computation cost of reinforcement learning algorithms. However, current methods mostly …
computation cost of reinforcement learning algorithms. However, current methods mostly …
Learning and retrieval from prior data for skill-based imitation learning
Imitation learning offers a promising path for robots to learn general-purpose behaviors, but
traditionally has exhibited limited scalability due to high data supervision requirements and …
traditionally has exhibited limited scalability due to high data supervision requirements and …
Meta-world: A benchmark and evaluation for multi-task and meta reinforcement learning
Meta-reinforcement learning algorithms can enable robots to acquire new skills much more
quickly, by leveraging prior experience to learn how to learn. However, much of the current …
quickly, by leveraging prior experience to learn how to learn. However, much of the current …
Hierarchical decision transformer
A Correia, LA Alexandre - 2023 IEEE/RSJ International …, 2023 - ieeexplore.ieee.org
Sequence models in reinforcement learning require task knowledge to estimate the task
policy. This paper presents the hierarchical decision transformer (HDT). HDT is a …
policy. This paper presents the hierarchical decision transformer (HDT). HDT is a …