policy-gradient

What is Policy-gradient

Policy-gradient methods can learn a stochastic policy while value functions can’t.

This has two consequences:

• We don’t need to implement an exploration/exploitation trade-off by hand. Since we output a probability distribution over actions, the agent explores the state space without always taking the same trajectory.

• We also get rid of the problem of perceptual aliasing. Perceptual aliasing is when two states seem (or are) the same but need different actions.

[!INFO] >perceptual aliasing 是因为 value based 的方式对于一个状态的最优解是固定的, 所以导致模型无法很好的处理要求对一个状态进行随机做出的决策。