Allen's REINFORCE notes
Contents
Links
Motivation
Recall that the objective of Reinforcement Learning is to find an optimal policy which we encode in a neural network with parameters . These optimal parameters are defined as
![{\displaystyle \theta ^{*}={\text{argmax}}_{\theta }E_{\tau \sim p_{\theta }(\tau )}\left[\sum _{t}r(s_{t},a_{t})\right]}](https://wikimedia.org/api/rest_v1/media/math/render/svg/74bb8656d55a9f9e2fc28ad99b15be9a11e8b3bb)
Learning
Learning involves the agent taking actions and the environment returning a new state and reward.
- Input: : States at each time step
- Output: : Actions at each time step
- Data:
- Learn to maximize
