This phenomenon takes place when the learning process begins to focus on classifying less “important” states. In this paper, we introduce a measure of state's ...
Abstract. We investigate the problem of using function approximation in rein- forcement learning where the agent's policy is represented as a classifier ...
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PDF | We investigate the problem of using function approximation in rein- forcement learning where the agent's policy is represented as a classifier.
Mar 18, 2023 · Let's say at the start of a game the ammo counter is 100. If the batch size is 32, are we passing the agent 32 state vectors all with ammo=100?
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Batch learning is concerned with problems when a learning algorithm must work with data collected in some manner that is not under the control of the learning ...
In this chapter, we introduce the basic principles and the theory behind batch reinforcement learning, describe the most important algorithms, exemplarily ...
Dec 11, 2022 · Reinforcement Learning Coach by Intel AI Lab enables easy experimentation with state of the art Reinforcement Learning algorithms ...
Batch reinforcement learning historically was defined as the class of algorithms developed for solving a particular learning problem, namely the batch ...
Aug 1, 2024 · In batch reinforcement learning (RL), also known as offline RL, the goal is to learn a good policy from a fixed dataset.
This paper examines the advantages that RL offers over the traditional model-based optimal control methods and how it can be tailored to better address the ...