Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Scientific Discovery and the Cost of Measurement -- Balancing
  Information and Cost in Reinforcement Learning   The use of reinforcement learning (RL) in scientific applications, such as
materials design and automated chemistry, is increasing. A major challenge,
however, lies in fact that measuring the state of the system is often costly
and time consuming in scientific applications, whereas policy learning with RL
requires a measurement after each time step. In this work, we make the
measurement costs explicit in the form of a costed reward and propose a
framework that enables off-the-shelf deep RL algorithms to learn a policy for
both selecting actions and determining whether or not to measure the current
state of the system at each time step. In this way, the agents learn to balance
the need for information with the cost of information. Our results show that
when trained under this regime, the Dueling DQN and PPO agents can learn
optimal action policies whilst making up to 50\% fewer state measurements, and
recurrent neural networks can produce a greater than 50\% reduction in
measurements. We postulate the these reduction can help to lower the barrier to
applying RL to real-world scientific applications.

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The categories of this paper are: acc-phys
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The categories of this paper are: alg-geom
32
The categories of this paper are: atom-ph
7
The categories of this paper are: hep-th
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The categories of this paper are: comp-gas
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The categories of this paper are: q-fin
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The categories of this paper are: adap-org
31
The categories of this paper are: ao-sci
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The categories of this paper are: stat
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The categories of this paper are: math-ph
34
The categories of this paper are: chem-ph
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The categories of this paper are: quant-ph
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The categories of this paper are: cond-mat
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The categories of this paper are: plasm-ph
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The categories of this paper are: eess
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The categories of this paper are: bayes-an