C51
A distributional reinforcement learning algorithm that approximates the return distribution using a categorical distribution. It is one of the foundational works in distributional reinforcement learning.
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Distributional reinforcement learning (DRL) learns the full distribution of returns rather than only their expectation, an idea first systematized by Bellemare et al. in the C51 algorithm.
The C51 algorithm, proposed by Bellemare et al. in 2017, is the first algorithm to successfully apply distributional reinforcement learning to Atari games; it approximates the return distribution with 51 fixed support points and updates the distribution through a projection step.
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Distributional reinforcement learning (DRL) learns the full distribution of returns rather than only their expectation, an idea first systematized by Bellemare et al. in the C51 algorithm.
50%UnverifiedThe C51 algorithm, proposed by Bellemare et al. in 2017, is the first algorithm to successfully apply distributional reinforcement learning to Atari games; it approximates the return distribution with 51 fixed support points and updates the distribution through a projection step.
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