inverse reinforcement learning(IRL)game theorygoal recognitionIn 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 ...
[论文浅读-AAMAS23]Multiagent Inverse Reinforcement Learning via Theory of Mind Reasoning Kid 心理4 人赞同了该文章 Q1论文试图解决什么问题?本文解决的是multi agent inverse reinforcement learning的问题 文中对于MIRL的引入是从adhoc的角度切入的,当第一次与队友完成cooperative task时,不知道队友的goal,strategy...
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Learning from demonstration, or imitation learning, is the process of learning to act in an environment from examples provided by a teacher. Inverse reinforcement learning (IRL) is a specific form of learning from demonstration that attempts to estimate the reward function of a Markov decision proce...
solution to the cooperative inverse reinforcement learning (CIRL) dynamic game based on well-established cognitive models of decision making and theory of ... JF Fisac,MA Gates,JB Hamrick,... 被引量: 8发表: 2017年 Where Do You Think You're Going?: Inferring Beliefs about Dynamics from Behav...
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 actin...
A Cascaded Supervised Learning Approach to Inverse Reinforcement Learning Edouard Klein1,2, Bilal Piot2,3, Matthieu Geist2, and Olivier Pietquin2,3,∗ 1 ABC Team LORIA-CNRS, France 2 Supélec, IMS-MaLIS Research Group, France firstname.lastname@supelec.fr 3 UMI 2958 (GeorgiaTech-CNRS), ...
EOSMA has many parameters and depending on the problem to be solved, parameter adaptive selection methods can be considered for the algorithm, such as parameter adaptive mechanism based on success history88 or parameter adaptive mechanism based on reinforcement learning89. In addition, EOSMA can be...
A schematic of the main features of reinforcement learning for nanostructure inverse design. Agent: An agent is a component that take actions on the environment. Actions: Actions (A) are a set of possible ways that the agent can interact with the environment. In the inverse design in nanophoto...
learning, pure exploration, constrained MDPs, offline RL, human-regularized RL, and others. Inverse reinforcement learning is a powerful paradigm that focuses on recovering an unknown reward function that can rationalize the observed behaviour of an agent. There has been recent theoretical advances in...