© 2020, Springer Science+Business Media, LLC, part of Springer Nature. Inverse reinforcement learning (IRL) is the problem of learning the preferences of an agent from observing its behavior on a task. It inverts RL which focuses on learning an agent’s behavior on a task based on the reward signals received. IRL is witnessing sustained attention due to promising applications in robotics, computer games, and finance, as well as in other sectors. Methods for IRL have, for the most part, focused on batch settings where the observed agent’s behavioral data has already been collected. However, the related problem of online IRL—where observations are incrementally accrued, yet the real-time demands of the application often prohibit a full rerun o...
Inverse Reinforcement Learning (IRL) is a subfield of Reinforcement Learning (RL) that focuses on re...
This paper proposes model-free imitation learning named Entropy-Regularized Imitation Learning (ERIL...
Inverse Reinforcement Learning (IRL) aims to recover a reward function from expert demonstrations in...
Inverse reinforcement learning (IRL) is the problem of learning the preferences of an agent from obs...
Based on the premise that the most succinct representation of the behavior of an entity is its rewar...
In real-world applications, inferring the intentions of expert agents (e.g., human operators) can be...
Robots had a great impact on the manufacturing industry ever since the early seventies when companie...
A major challenge faced by machine learning community is the decision making problems under uncertai...
In decision-making problems reward function plays an important role in finding the best policy. Rein...
This purpose of this paper is to provide an overview of the theoretical background and applications ...
Abstract Reinforcement Learning (RL) is a method that helps programming an autonomous agent through...
This paper proposes model-free imitation learning named Entropy-Regularized Imitation Learning (ERIL...
Inverse reinforcement learning (IRL) allows autonomous agents to learn to solve complex tasks from s...
Inverse Reinforcement Learning (IRL) is attractive in scenarios where reward engineering can be tedi...
In traditional Reinforcement Learning (RL) [4], a single agent learns to act in an environment by op...
Inverse Reinforcement Learning (IRL) is a subfield of Reinforcement Learning (RL) that focuses on re...
This paper proposes model-free imitation learning named Entropy-Regularized Imitation Learning (ERIL...
Inverse Reinforcement Learning (IRL) aims to recover a reward function from expert demonstrations in...
Inverse reinforcement learning (IRL) is the problem of learning the preferences of an agent from obs...
Based on the premise that the most succinct representation of the behavior of an entity is its rewar...
In real-world applications, inferring the intentions of expert agents (e.g., human operators) can be...
Robots had a great impact on the manufacturing industry ever since the early seventies when companie...
A major challenge faced by machine learning community is the decision making problems under uncertai...
In decision-making problems reward function plays an important role in finding the best policy. Rein...
This purpose of this paper is to provide an overview of the theoretical background and applications ...
Abstract Reinforcement Learning (RL) is a method that helps programming an autonomous agent through...
This paper proposes model-free imitation learning named Entropy-Regularized Imitation Learning (ERIL...
Inverse reinforcement learning (IRL) allows autonomous agents to learn to solve complex tasks from s...
Inverse Reinforcement Learning (IRL) is attractive in scenarios where reward engineering can be tedi...
In traditional Reinforcement Learning (RL) [4], a single agent learns to act in an environment by op...
Inverse Reinforcement Learning (IRL) is a subfield of Reinforcement Learning (RL) that focuses on re...
This paper proposes model-free imitation learning named Entropy-Regularized Imitation Learning (ERIL...
Inverse Reinforcement Learning (IRL) aims to recover a reward function from expert demonstrations in...
Add to ORKG