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Cheatsheet of Latex Code for Reinforcement Learning Equations
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In this blog, we will summarize the latex code of most fundamental equations of reinforcement learning (RL). This blog will cover many topics, including Bellman Equation, Markov Decision Process(MDP), Partial Observable Markov Decision Process(POMDP), DQN, A3C, etc.
1. Reinforcement learning
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1.1 Bellman Equation
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