深度学习入门(9) - Reinforcement Learning 强化学习

Reinforcement Learning

an agent performs actions in environment, and receives rewards

goal: Learn how to take actions that maximize reward

Stochasticity: Rewards and state transitions may be random

Credit assignment: Reward $r_t$ may not directly depend on action $a_t$

Nondifferentiable: Can’t backprop through the world

Nonstationary: What the agent experiences depends on how it acts

Markov Decision Process (MDP)

Mathematical formalization of the RL problem: A tuple $(S,A,R,P,\gamma )$

SS