International audienceThis paper deals with the Inverse Reinforcement Learning framework, whose purpose is to learn control policies from demonstrations by an expert. This method inferes from demonstrations a utility function the expert is allegedly maximizing. In this paper we map the reward space into a subset of smaller dimensionality without loss of generality for all Markov Decision Processes (MDPs). We then present three experimental results showing both the promising aspect of the application of this result to existing IRL methods and its shortcomings. We conclude with considerations on further research
A major challenge faced by machine learning community is the decision making problems under uncertai...
Inverse Reinforcement Learning (IRL) deals with the problem of recovering the reward function optimi...
International audienceThis paper addresses the Inverse Reinforcement Learning (IRL) problem which is...
International audienceThis paper deals with the Inverse Reinforcement Learning framework, whose purp...
In decision-making problems reward function plays an important role in finding the best policy. Rein...
We study the problem of learning a policy in a Markov decision process (MDP) based on observations o...
Reward learning from demonstration is the task of inferring the intents or goals of an agent demonst...
Learning desirable behavior from a limited number of demonstrations, also known as inverse reinforce...
Inverse Reinforcement Learning (IRL) aims to recover a reward function from expert demonstrations in...
This work handles the inverse reinforcement learning (IRL) problem where only a small number of demo...
We consider learning in a Markov decision process where we are not explicitly given a reward functio...
This purpose of this paper is to provide an overview of the theoretical background and applications ...
Reinforcement Learning (RL) methods provide a solution for decision-making problems under uncertaint...
Inverse reinforcement learning (IRL) addresses the problem of re-covering the unknown reward functio...
We consider the problem of imitation learning where the examples, demonstrated by an expert, cover o...
A major challenge faced by machine learning community is the decision making problems under uncertai...
Inverse Reinforcement Learning (IRL) deals with the problem of recovering the reward function optimi...
International audienceThis paper addresses the Inverse Reinforcement Learning (IRL) problem which is...
International audienceThis paper deals with the Inverse Reinforcement Learning framework, whose purp...
In decision-making problems reward function plays an important role in finding the best policy. Rein...
We study the problem of learning a policy in a Markov decision process (MDP) based on observations o...
Reward learning from demonstration is the task of inferring the intents or goals of an agent demonst...
Learning desirable behavior from a limited number of demonstrations, also known as inverse reinforce...
Inverse Reinforcement Learning (IRL) aims to recover a reward function from expert demonstrations in...
This work handles the inverse reinforcement learning (IRL) problem where only a small number of demo...
We consider learning in a Markov decision process where we are not explicitly given a reward functio...
This purpose of this paper is to provide an overview of the theoretical background and applications ...
Reinforcement Learning (RL) methods provide a solution for decision-making problems under uncertaint...
Inverse reinforcement learning (IRL) addresses the problem of re-covering the unknown reward functio...
We consider the problem of imitation learning where the examples, demonstrated by an expert, cover o...
A major challenge faced by machine learning community is the decision making problems under uncertai...
Inverse Reinforcement Learning (IRL) deals with the problem of recovering the reward function optimi...
International audienceThis paper addresses the Inverse Reinforcement Learning (IRL) problem which is...
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