How Does Value Distribution in Distributional Reinforcement Learning
Help Optimization?
- URL: http://arxiv.org/abs/2209.14513v1
- Date: Thu, 29 Sep 2022 02:18:31 GMT
- Title: How Does Value Distribution in Distributional Reinforcement Learning
Help Optimization?
- Authors: Ke Sun, Bei Jiang, Linglong Kong
- Abstract summary: We consider the problem of learning a set of probability distributions from the Bellman dynamics in distributional reinforcement learning(RL)
Despite its success to obtain superior performance, we still have a poor understanding of how the value distribution in distributional RL works.
- Score: 4.695760312524447
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: We consider the problem of learning a set of probability distributions from
the Bellman dynamics in distributional reinforcement learning~(RL) that learns
the whole return distribution compared with only its expectation in classical
RL. Despite its success to obtain superior performance, we still have a poor
understanding of how the value distribution in distributional RL works. In this
study, we analyze the optimization benefits of distributional RL by leverage of
additional value distribution information over classical RL in the Neural
Fitted Z-Iteration~(Neural FZI) framework. To begin with, we demonstrate that
the distribution loss of distributional RL has desirable smoothness
characteristics and hence enjoys stable gradients, which is in line with its
tendency to promote optimization stability. Furthermore, the acceleration
effect of distributional RL is revealed by decomposing the return distribution.
It turns out that distributional RL can perform favorably if the value
distribution approximation is appropriate, measured by the variance of gradient
estimates in each environment for any specific distributional RL algorithm.
Rigorous experiments validate the stable optimization behaviors of
distributional RL, contributing to its acceleration effects compared to
classical RL. The findings of our research illuminate how the value
distribution in distributional RL algorithms helps the optimization.
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