1. 基础概念

Markov Decision Process (MDP)

• Sets

  • State: $\mathcal{S}$

  • Action: $\mathcal{A}(s)$

  • Reward: $\mathcal{R}(s,a)$

• Probability distribution

  • State transition: $p(s'|s,a)$

  • Reward: $p(r|s,a)$

• Policy: $\pi(a|s)$

• Markov property