In real-time strategy(RTS)games,the ability of recognizing other players’goals is important for creating artifical intelligence(AI)players.However,most current goal recognition methods do not take the player’s decep...In real-time strategy(RTS)games,the ability of recognizing other players’goals is important for creating artifical intelligence(AI)players.However,most current goal recognition methods do not take the player’s deceptive behavior into account which often occurs in RTS game scenarios,resulting in poor recognition results.In order to solve this problem,this paper proposes goal recognition for deceptive agent,which is an extended goal recognition method applying the deductive reason method(from general to special)to model the deceptive agent’s behavioral strategy.First of all,the general deceptive behavior model is proposed to abstract features of deception,and then these features are applied to construct a behavior strategy that best matches the deceiver’s historical behavior data by the inverse reinforcement learning(IRL)method.Final,to interfere with the deceptive behavior implementation,we construct a game model to describe the confrontation scenario and the most effective interference measures.展开更多
We improve inverse reinforcement learning(IRL) by applying dimension reduction methods to automatically extract Abstract features from human-demonstrated policies,to deal with the cases where features are either unkno...We improve inverse reinforcement learning(IRL) by applying dimension reduction methods to automatically extract Abstract features from human-demonstrated policies,to deal with the cases where features are either unknown or numerous.The importance rating of each abstract feature is incorporated into the reward function.Simulation is performed on a task of driving in a five-lane highway,where the controlled car has the largest fixed speed among all the cars.Performance is almost 10.6% better on average with than without importance ratings.展开更多
Interest in inverse reinforcement learning (IRL) has recently increased,that is,interest in the problem of recovering the reward function underlying a Markov decision process (MDP) given the dynamics of the system and...Interest in inverse reinforcement learning (IRL) has recently increased,that is,interest in the problem of recovering the reward function underlying a Markov decision process (MDP) given the dynamics of the system and the behavior of an expert.This paper deals with an incremental approach to online IRL.First,the convergence property of the incremental method for the IRL problem was investigated,and the bounds of both the mistake number during the learning process and regret were provided by using a detailed proof.Then an online algorithm based on incremental error correcting was derived to deal with the IRL problem.The key idea is to add an increment to the current reward estimate each time an action mismatch occurs.This leads to an estimate that approaches a target optimal value.The proposed method was tested in a driving simulation experiment and found to be able to efficiently recover an adequate reward function.展开更多
Purpose-This purpose of this paper is to provide an overview of the theoretical background and applications of inverse reinforcement learning(IRL).Design/methodology/approach-Reinforcement learning(RL)techniques provi...Purpose-This purpose of this paper is to provide an overview of the theoretical background and applications of inverse reinforcement learning(IRL).Design/methodology/approach-Reinforcement learning(RL)techniques provide a powerful solution for sequential decision making problems under uncertainty.RL uses an agent equipped with a reward function to find a policy through interactions with a dynamic environment.However,one major assumption of existing RL algorithms is that reward function,the most succinct representation of the designer’s intention,needs to be provided beforehand.In practice,the reward function can be very hard to specify and exhaustive to tune for large and complex problems,and this inspires the development of IRL,an extension of RL,which directly tackles this problem by learning the reward function through expert demonstrations.In this paper,the original IRL algorithms and its close variants,as well as their recent advances are reviewed and compared.Findings-This paper can serve as an introduction guide of fundamental theory and developments,as well as the applications of IRL.Originality/value-This paper surveys the theories and applications of IRL,which is the latest development of RL and has not been done so far.展开更多
文摘In real-time strategy(RTS)games,the ability of recognizing other players’goals is important for creating artifical intelligence(AI)players.However,most current goal recognition methods do not take the player’s deceptive behavior into account which often occurs in RTS game scenarios,resulting in poor recognition results.In order to solve this problem,this paper proposes goal recognition for deceptive agent,which is an extended goal recognition method applying the deductive reason method(from general to special)to model the deceptive agent’s behavioral strategy.First of all,the general deceptive behavior model is proposed to abstract features of deception,and then these features are applied to construct a behavior strategy that best matches the deceiver’s historical behavior data by the inverse reinforcement learning(IRL)method.Final,to interfere with the deceptive behavior implementation,we construct a game model to describe the confrontation scenario and the most effective interference measures.
文摘We improve inverse reinforcement learning(IRL) by applying dimension reduction methods to automatically extract Abstract features from human-demonstrated policies,to deal with the cases where features are either unknown or numerous.The importance rating of each abstract feature is incorporated into the reward function.Simulation is performed on a task of driving in a five-lane highway,where the controlled car has the largest fixed speed among all the cars.Performance is almost 10.6% better on average with than without importance ratings.
基金Project (No.90820306) supported by the National Natural Science Foundation of China
文摘Interest in inverse reinforcement learning (IRL) has recently increased,that is,interest in the problem of recovering the reward function underlying a Markov decision process (MDP) given the dynamics of the system and the behavior of an expert.This paper deals with an incremental approach to online IRL.First,the convergence property of the incremental method for the IRL problem was investigated,and the bounds of both the mistake number during the learning process and regret were provided by using a detailed proof.Then an online algorithm based on incremental error correcting was derived to deal with the IRL problem.The key idea is to add an increment to the current reward estimate each time an action mismatch occurs.This leads to an estimate that approaches a target optimal value.The proposed method was tested in a driving simulation experiment and found to be able to efficiently recover an adequate reward function.
文摘Purpose-This purpose of this paper is to provide an overview of the theoretical background and applications of inverse reinforcement learning(IRL).Design/methodology/approach-Reinforcement learning(RL)techniques provide a powerful solution for sequential decision making problems under uncertainty.RL uses an agent equipped with a reward function to find a policy through interactions with a dynamic environment.However,one major assumption of existing RL algorithms is that reward function,the most succinct representation of the designer’s intention,needs to be provided beforehand.In practice,the reward function can be very hard to specify and exhaustive to tune for large and complex problems,and this inspires the development of IRL,an extension of RL,which directly tackles this problem by learning the reward function through expert demonstrations.In this paper,the original IRL algorithms and its close variants,as well as their recent advances are reviewed and compared.Findings-This paper can serve as an introduction guide of fundamental theory and developments,as well as the applications of IRL.Originality/value-This paper surveys the theories and applications of IRL,which is the latest development of RL and has not been done so far.
