Allen's REINFORCE notes

Allen's REINFORCE notes

Links

• RLbook2020

Motivation

Recall that the objective of Reinforcement Learning is to find an optimal policy Failed to parse (unknown function "\math"): {\displaystyle \pi^*<\math> which we encode in a neural network with parameters <math>\theta^*<\math>. These optimal parameters are defined as <math>\theta^* = \text<argmax>_\theta E_{\tau \sim p_\theta(\tau)} \left[ \sum_t r(s_t, a_t) \right] <\math> === Learning === Learning involves the agent taking actions and the environment returning a new state and reward. * Input: <math>s_t} : States at each time step

• Output: $a_{t}$: Actions at each time step
• Data: $(s_{1},a_{1},r_{1},...,s_{T},a_{T},r_{T})$
• Learn $\pi _{\theta }:s_{t}->a_{t}$ to maximize $\sum _{t}r_{t}$