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Strictly batch imitation learning by energy-based distribution matching
Consider learning a policy purely on the basis of demonstrated behavior---that is, with no
access to reinforcement signals, no knowledge of transition dynamics, and no further …
access to reinforcement signals, no knowledge of transition dynamics, and no further …
Hybrid residual multiexpert reinforcement learning for spatial scheduling of high-density parking lots
Industries, such as manufacturing, are accelerating their embrace of the metaverse to
achieve higher productivity, especially in complex industrial scheduling. In view of the …
achieve higher productivity, especially in complex industrial scheduling. In view of the …
Inverse decision modeling: Learning interpretable representations of behavior
Decision analysis deals with modeling and enhancing decision processes. A principal
challenge in improving behavior is in obtaining a transparent* description* of existing …
challenge in improving behavior is in obtaining a transparent* description* of existing …
Dealing with multiple experts and non-stationarity in inverse reinforcement learning: an application to real-life problems
In real-world applications, inferring the intentions of expert agents (eg, human operators)
can be fundamental to understand how possibly conflicting objectives are managed, hel** …
can be fundamental to understand how possibly conflicting objectives are managed, hel** …
Truly batch model-free inverse reinforcement learning about multiple intentions
Abstract We consider Inverse Reinforcement Learning (IRL) about multiple intentions,\ie the
problem of estimating the unknown reward functions optimized by a group of experts that …
problem of estimating the unknown reward functions optimized by a group of experts that …
Robust learning from demonstrations with mixed qualities using leveraged gaussian processes
In this paper, we focus on the problem of learning from demonstration (LfD) where
demonstrations with different proficiencies are provided without labeling. To this end, we …
demonstrations with different proficiencies are provided without labeling. To this end, we …
Inferring the strategy of offensive and defensive play in soccer with inverse reinforcement learning
Analyzing and understanding strategies applied by top soccer teams has always been in the
focus of coaches, scouts, players, and other sports professionals. Although the game …
focus of coaches, scouts, players, and other sports professionals. Although the game …
Policy space identification in configurable environments
We study the problem of identifying the policy space available to an agent in a learning
process, having access to a set of demonstrations generated by the agent playing the …
process, having access to a set of demonstrations generated by the agent playing the …
[ספר][B] Exploiting environment configurability in reinforcement learning
AM Metelli - 2022 - books.google.com
In recent decades, Reinforcement Learning (RL) has emerged as an effective approach to
address complex control tasks. In a Markov Decision Process (MDP), the framework typically …
address complex control tasks. In a Markov Decision Process (MDP), the framework typically …
On the use of the policy gradient and hessian in inverse reinforcement learning
Reinforcement Learning (RL) is an effective approach to solve sequential decision making
problems when the environment is equipped with a reward function to evaluate the agent's …
problems when the environment is equipped with a reward function to evaluate the agent's …