Results 1 - 10 of 12,029

Algorithms for Inverse Reinforcement Learning

by Andrew Y. Ng, Stuart Russell - 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 ..."
Abstract - Cited by 314 (6 self) - Add to MetaCart
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

by Deepak Ramachandran - 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 ..."
Abstract - Cited by 86 (0 self) - Add to MetaCart
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

by Pieter Abbeel, Andrew Y. Ng - In Proceedings of the Twenty-first 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 ..."
Abstract - Cited by 382 (12 self) - Add to MetaCart
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

by Valdinei Freire Da Silva, Anna Helena, Reali Costa, Inteligentes Escola Politécnica, Pedro Lima - 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 ..."
Abstract - Cited by 4 (0 self) - Add to MetaCart
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

by Arthur Fischer, Technische Universität Darmstadt
"... 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 ..."
Abstract - Add to MetaCart
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

by Constantin A. Rothkopf, Christos Dimitrakakis , 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 ..."
Abstract - Cited by 5 (0 self) - Add to MetaCart
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

Multi-Agent Inverse Reinforcement Learning

by Sriraam Natarajan, Gautam Kunapuli, Kshitij Judah, Prasad Tadepalli, Kristian Kersting, Jude Shavlik
"... 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 ..."
Abstract - Cited by 7 (1 self) - Add to MetaCart
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

Inverse Reinforcement Learning from Failure

by Kyriacos Shiarlis , João Messias , Maarten Van Someren , Shimon Whiteson
"... Abstract-In this paper, we approach the problem of Inverse Reinforcement Learning (IRL) from a rather different perspective. Instead of trying to only mimic an expert as in traditional IRL, we present a method that can also utilise failed or bad demonstrations of a task. In particular, we propose a ..."
Abstract - Add to MetaCart
Abstract-In this paper, we approach the problem of Inverse Reinforcement Learning (IRL) from a rather different perspective. Instead of trying to only mimic an expert as in traditional IRL, we present a method that can also utilise failed or bad demonstrations of a task. In particular, we propose

Bayesian multitask inverse reinforcement learning

by Christos Dimitrakakis, Constantin A. Rothkopf
"... 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 ..."
Abstract - Cited by 12 (4 self) - Add to MetaCart
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

by Abdeslam Boularias, Jens Kober, Jan Peters
"... 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 ..."
Abstract - Cited by 29 (3 self) - Add to MetaCart
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
Results 1 - 10 of 12,029