Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Multi-step Greedy Reinforcement Learning Algorithms   Multi-step greedy policies have been extensively used in model-based
reinforcement learning (RL), both when a model of the environment is available
(e.g.,~in the game of Go) and when it is learned. In this paper, we explore
their benefits in model-free RL, when employed using multi-step dynamic
programming algorithms: $\kappa$-Policy Iteration ($\kappa$-PI) and
$\kappa$-Value Iteration ($\kappa$-VI). These methods iteratively compute the
next policy ($\kappa$-PI) and value function ($\kappa$-VI) by solving a
surrogate decision problem with a shaped reward and a smaller discount factor.
We derive model-free RL algorithms based on $\kappa$-PI and $\kappa$-VI in
which the surrogate problem can be solved by any discrete or continuous action
RL method, such as DQN and TRPO. We identify the importance of a
hyper-parameter that controls the extent to which the surrogate problem is
solved and suggest a way to set this parameter. When evaluated on a range of
Atari and MuJoCo benchmark tasks, our results indicate that for the right range
of $\kappa$, our algorithms outperform DQN and TRPO. This shows that our
multi-step greedy algorithms are general enough to be applied over any existing
RL algorithm and can significantly improve its performance.

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The categories of this paper are: q-fin
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The categories of this paper are: eess
27
The categories of this paper are: comp-gas
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The categories of this paper are: nucl-th
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The categories of this paper are: gr-qc
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The categories of this paper are: hep-ex
3
The categories of this paper are: physics
35
The categories of this paper are: mtrl-th
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The categories of this paper are: nucl-ex
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The categories of this paper are: alg-geom
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The categories of this paper are: q-alg
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The categories of this paper are: math-ph
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The categories of this paper are: acc-phys
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The categories of this paper are: hep-ph
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The categories of this paper are: chem-ph
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The categories of this paper are: dg-ga