Add to ORKGWe evaluate the robustness of reward functions learned with IRL, when transferred to similar tasks. We exceed state of the art results for one benchmark task and solve another one for the first time. Modifications are proposed that achieve faster and more stable training
In decision-making problems reward function plays an important role in finding the best policy. Rein...
This work handles the inverse reinforcement learning (IRL) problem where only a small number of demo...
Inverse reinforcement learning (IRL) seeks to learn the reward function from expert trajectories, to...
We evaluate the robustness of reward functions learned with IRL, when transferred to similar tasks. ...
International audienceInverse Reinforcement Learning (IRL) is an effective approach to recover a rew...
Reinforcement Learning (RL) methods provide a solution for decision-making problems under uncertaint...
We propose a novel formulation for the Inverse Reinforcement Learning (IRL) problem, which jointly a...
This purpose of this paper is to provide an overview of the theoretical background and applications ...
A major challenge faced by machine learning community is the decision making problems under uncertai...
The task of learning a reward function from expert demonstrations suffers from high sample complexit...
Inverse Reinforcement Learning (IRL) learns an optimal policy, given some expert demonstrations, thu...
In real-world applications, inferring the intentions of expert agents (e.g., human operators) can be...
International audienceThis paper considers the Inverse Reinforcement Learning (IRL) problem, that is...
Inverse reinforcement learning (IRL) denotes a powerful family of algorithms for recovering a reward...
One of the fundamental problems of artificial intelligence is learning how to behave optimally. With...
In decision-making problems reward function plays an important role in finding the best policy. Rein...
This work handles the inverse reinforcement learning (IRL) problem where only a small number of demo...
Inverse reinforcement learning (IRL) seeks to learn the reward function from expert trajectories, to...
We evaluate the robustness of reward functions learned with IRL, when transferred to similar tasks. ...
International audienceInverse Reinforcement Learning (IRL) is an effective approach to recover a rew...
Reinforcement Learning (RL) methods provide a solution for decision-making problems under uncertaint...
We propose a novel formulation for the Inverse Reinforcement Learning (IRL) problem, which jointly a...
This purpose of this paper is to provide an overview of the theoretical background and applications ...
A major challenge faced by machine learning community is the decision making problems under uncertai...
The task of learning a reward function from expert demonstrations suffers from high sample complexit...
Inverse Reinforcement Learning (IRL) learns an optimal policy, given some expert demonstrations, thu...
In real-world applications, inferring the intentions of expert agents (e.g., human operators) can be...
International audienceThis paper considers the Inverse Reinforcement Learning (IRL) problem, that is...
Inverse reinforcement learning (IRL) denotes a powerful family of algorithms for recovering a reward...
One of the fundamental problems of artificial intelligence is learning how to behave optimally. With...
In decision-making problems reward function plays an important role in finding the best policy. Rein...
This work handles the inverse reinforcement learning (IRL) problem where only a small number of demo...
Inverse reinforcement learning (IRL) seeks to learn the reward function from expert trajectories, to...