Bellman Equation
Tags: #machine learningEquation
$$v_{\pi}(s)=\sum_{a}\pi(a|s)\sum_{s^{'},r}p(s^{'},r|s,a)[r+\gamma v_{\pi}(s^{'})]$$Latex Code
v_{\pi}(s)=\sum_{a}\pi(a|s)\sum_{s^{'},r}p(s^{'},r|s,a)[r+\gamma v_{\pi}(s^{'})]
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Introduction
Equation
Latex Code
v_{\pi}(s)=\sum_{a}\pi(a|s)\sum_{s^{'},r}p(s^{'},r|s,a)[r+\gamma v_{\pi}(s^{'})]
Explanation
: Value at state s in policy \pi
: Value at state s^{'} in policy \pi
: Probability of choosing action a given state s
: Reward at state s
: Reward discount factor \gamma
You can check more detailed information of Bellman Equation in this tutorial Introduction to Reinforcement Learning for more details.
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