Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Reinforcement Learning with a Corrupted Reward Channel   No real-world reward function is perfect. Sensory errors and software bugs
may result in RL agents observing higher (or lower) rewards than they should.
For example, a reinforcement learning agent may prefer states where a sensory
error gives it the maximum reward, but where the true reward is actually small.
We formalise this problem as a generalised Markov Decision Problem called
Corrupt Reward MDP. Traditional RL methods fare poorly in CRMDPs, even under
strong simplifying assumptions and when trying to compensate for the possibly
corrupt rewards. Two ways around the problem are investigated. First, by giving
the agent richer data, such as in inverse reinforcement learning and
semi-supervised reinforcement learning, reward corruption stemming from
systematic sensory errors may sometimes be completely managed. Second, by using
randomisation to blunt the agent's optimisation, reward corruption can be
partially managed under some assumptions.

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The categories of this paper are: patt-sol
29
The categories of this paper are: funct-an
17
The categories of this paper are: q-fin
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The categories of this paper are: chao-dyn
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The categories of this paper are: supr-con
34
The categories of this paper are: chem-ph
5
The categories of this paper are: gr-qc
15
The categories of this paper are: nucl-ex
35
The categories of this paper are: mtrl-th
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The categories of this paper are: cmp-lg
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The categories of this paper are: q-bio
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The categories of this paper are: alg-geom
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The categories of this paper are: ao-sci
30
The categories of this paper are: q-alg
3
The categories of this paper are: physics
20
The categories of this paper are: acc-phys