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Allen's REINFORCE notes

Revision as of 00:03, 25 May 2024 by Allen12 (talk | contribs)

Allen's REINFORCE notes

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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  . 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 ( ) determined by the policy is the highest over all policies.

Overview

  1. Initialize neural network with input dimensions = observation dimensions and output dimensions = action dimensions. Remember a policy is a mapping from observations to outputs. If the space is continuous, it may make more sense to make output be one mean and one standard deviation for each component of the action.

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

Loss Function