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
• 1.1 Bellman Equation

• 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
  You can check more detailed information of Bellman Equation in this tutorial Introduction to Reinforcement Learning for more details.