Inverse Reinforcement Learning with Simultaneous Estimation of Rewards and Dynamics - Supplementary Material
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چکیده
This document contains supplementary material to the paper Inverse Reinforcement Learning with Simultaneous Estimation of Rewards and Dynamics with more detailed derivations, additional proofs to lemmata and theorems as well as larger illustrations and plots of the evaluation task. 1 Partial Derivative of the Policy
منابع مشابه
Inverse Reinforcement Learning with Simultaneous Estimation of Rewards and Dynamics
Inverse Reinforcement Learning (IRL) describes the problem of learning an unknown reward function of a Markov Decision Process (MDP) from observed behavior of an agent. Since the agent’s behavior originates in its policy and MDP policies depend on both the stochastic system dynamics as well as the reward function, the solution of the inverse problem is significantly influenced by both. Current ...
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Reinforcement learning provides a powerful and general framework for decision making and control, but its application in practice is often hindered by the need for extensive feature and reward engineering. Deep reinforcement learning methods can remove the need for explicit engineering of policy or value features, but still require a manually specified reward function. Inverse reinforcement lea...
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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) and by the task of apprenticeship learning (learning policies from an expert). In this paper we sh...
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تاریخ انتشار 2016