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Algorithms for Inverse Reinforcement Learning
 in Proc. 17th International Conf. on Machine Learning
, 2000
"... This paper addresses the problem of inverse reinforcement learning (IRL) in Markov decision processes, that is, the problem of extracting a reward function given observed, optimal behaviour. IRL may be useful for apprenticeship learning to acquire skilled behaviour, and for ascertaining the re ..."
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Cited by 307 (6 self)
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This paper addresses the problem of inverse reinforcement learning (IRL) in Markov decision processes, that is, the problem of extracting a reward function given observed, optimal behaviour. IRL may be useful for apprenticeship learning to acquire skilled behaviour, and for ascertaining
Bayesian inverse reinforcement learning
 in 20th Int. Joint Conf. Artificial Intelligence
, 2007
"... Inverse Reinforcement Learning (IRL) is the problem of learning the reward function underlying a Markov Decision Process given the dynamics of the system and the behaviour of an expert. IRL is motivated by situations where knowledge of the rewards is a goal by itself (as in preference elicitation) a ..."
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Cited by 80 (0 self)
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Inverse Reinforcement Learning (IRL) is the problem of learning the reward function underlying a Markov Decision Process given the dynamics of the system and the behaviour of an expert. IRL is motivated by situations where knowledge of the rewards is a goal by itself (as in preference elicitation
Apprenticeship Learning via Inverse Reinforcement Learning
 In Proceedings of the Twentyfirst International Conference on Machine Learning
, 2004
"... We consider learning in a Markov decision process where we are not explicitly given a reward function, but where instead we can observe an expert demonstrating the task that we want to learn to perform. This setting is useful in applications (such as the task of driving) where it may be di#cul ..."
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Cited by 371 (11 self)
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by the expert. Our algorithm is based on using "inverse reinforcement learning" to try to recover the unknown reward function. We show that our algorithm terminates in a small number of iterations, and that even though we may never recover the expert's reward function, the policy output
Inverse reinforcement learning with evaluation
 in Proc. IEEE Int. Conf. Robot. Autom
"... Abstract Reinforcement Learning (RL) is a method that helps programming an autonomous agent through humanlike objectives as reinforcements, where the agent is responsible for discovering the best actions to full the objectives. Nevertheless, it is not easy to disentangle human objectives in reinfor ..."
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Cited by 4 (0 self)
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in reinforcement like objectives. Inverse Reinforcement Learning (IRL) determines the reinforcements that a given agent behaviour is fullling from the observation of the desired behaviour. In this paper we present a variant of IRL, which is called IRL with Evaluation (IRLE) where instead of observing the desired
Inverse Reinforcement Learning
"... Recently researches on imitation learning have shown that Markov Decision Processes (MDPs) are a powerful way to characterize this problem. Inverse reinforcement learning tries to describe observed behavior by ascertaining a reward function (or respectively a cost function) by solving a Markov Decis ..."
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Recently researches on imitation learning have shown that Markov Decision Processes (MDPs) are a powerful way to characterize this problem. Inverse reinforcement learning tries to describe observed behavior by ascertaining a reward function (or respectively a cost function) by solving a Markov
Preference elicitation and inverse reinforcement learning
, 2011
"... Abstract. We state the problem of inverse reinforcement learning in terms of preference elicitation, resulting in a principled (Bayesian) statistical formulation. This generalises previous work on Bayesian inverse reinforcement learning and allows us to obtain a posterior distribution on the agent’s ..."
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Cited by 5 (0 self)
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Abstract. We state the problem of inverse reinforcement learning in terms of preference elicitation, resulting in a principled (Bayesian) statistical formulation. This generalises previous work on Bayesian inverse reinforcement learning and allows us to obtain a posterior distribution on the agent
MultiAgent Inverse Reinforcement Learning
"... Learning the reward function of an agent by observing its behavior is termed inverse reinforcement learning and has applications in learning from demonstration or apprenticeship learning. We introduce the problem of multiagent inverse reinforcement learning, where reward functions of multiple agents ..."
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Cited by 7 (1 self)
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Learning the reward function of an agent by observing its behavior is termed inverse reinforcement learning and has applications in learning from demonstration or apprenticeship learning. We introduce the problem of multiagent inverse reinforcement learning, where reward functions of multiple
Bayesian multitask inverse reinforcement learning
"... Abstract. We generalise the problem of inverse reinforcement learning to multiple tasks, from a set of demonstrations. Each demonstration may represent one expert trying to solve a different task. Alternatively, one may see each demonstration as given by a different expert trying to solve the same t ..."
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Cited by 11 (3 self)
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Abstract. We generalise the problem of inverse reinforcement learning to multiple tasks, from a set of demonstrations. Each demonstration may represent one expert trying to solve a different task. Alternatively, one may see each demonstration as given by a different expert trying to solve the same
Relative Entropy Inverse Reinforcement Learning
"... We consider the problem of imitation learning where the examples, demonstrated by an expert, cover only a small part of a large state space. Inverse Reinforcement Learning (IRL) provides an efficient tool for generalizing the demonstration, based on the assumption that the expert is optimally acting ..."
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Cited by 27 (3 self)
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We consider the problem of imitation learning where the examples, demonstrated by an expert, cover only a small part of a large state space. Inverse Reinforcement Learning (IRL) provides an efficient tool for generalizing the demonstration, based on the assumption that the expert is optimally
Maximum entropy inverse reinforcement learning
 In Proc. AAAI
, 2008
"... Recent research has shown the benefit of framing problems of imitation learning as solutions to Markov Decision Problems. This approach reduces learning to the problem of recovering a utility function that makes the behavior induced by a nearoptimal policy closely mimic demonstrated behavior. In th ..."
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Cited by 109 (20 self)
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Recent research has shown the benefit of framing problems of imitation learning as solutions to Markov Decision Problems. This approach reduces learning to the problem of recovering a utility function that makes the behavior induced by a nearoptimal policy closely mimic demonstrated behavior
Results 1  10
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678,936