强化学习

• goal：learn how to take actions maximize reward

• agent and environment

• environment–>state–>agent–>action–>environment–>reward&next state–>agent

• example :

  1. cart-pole problem(倒立摆问题)
     list the Objective State Action & Reward
  2. Robot locomotion
  3. Atari Games
  4. go

• markov decision process （无记忆性）
  S,A,R,P,Y
  definition: Value function and Q-value function
  how good is a state??? && how good a state-action pair???
  Bellman equation：如果我们之前的状态选择是最大的，那么总体也是最优的
  the optimal policy is a policy that every step is optimal
  function Q is a very complex function and we want to use a neural network to approximate the function.
  Training the Q-network:Experience Replay
  弄一下放到一个集合里，再选取一个batch，以其为集合作为训练集。
  论文关于Q-learning

• Spiking NN 脉冲神经网络
  脉冲神经网络
  少数派报告