Allen's REINFORCE notes

Allen's REINFORCE notes

Links

• RLbook2020

Motivation

Recall that the objective of Reinforcement Learning is to find an optimal policy $\pi ^{*}$ which we encode in a neural network with parameters $\theta ^{*}$. $\pi _{\theta }$ is a mapping from observations to actions. These optimal parameters are defined as $\theta ^{*}={\text{argmax}}_{\theta }E_{\tau \sim p_{\theta }(\tau )}\left[\sum _{t}r(s_{t},a_{t})\right]$. Let's unpack what this means. To phrase it in english, this is basically saying that the optimal policy is one such that the expected value of the total reward over following a trajectory ($\tau$) determined by the policy is the highest over all policies.

Overview

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1 Initialize neural network with input dimensions = observation dimensions and output dimensions = action dimensions
2 For # of episodes:
3   While not terminated:
4     Get observation from environment
5     Use policy network to map observation to action distribution
6     Randomly sample one action from action distribution
7     Compute logarithmic probability of that action occurring
8     Step environment using action and store reward
9   Calculate loss over entire trajectory as function of probabilities and rewards