Predecessor Features

TL;DR

Predecessor Features is a reinforcement learning method that improves credit assignment by propagating TD errors to a broader set of predecessor states, enhancing learning efficiency especially with feature representations.

Contribution

The paper introduces Predecessor Features, a novel algorithm that extends credit assignment to all viable preceding states, not just recent ones, improving learning speed in RL.

Findings

Predecessor Features outperforms traditional eligibility traces in various environments.

The method effectively extends from tabular to feature-based representations.

It achieves faster convergence and better performance compared to existing approaches.

Abstract

Any reinforcement learning system must be able to identify which past events contributed to observed outcomes, a problem known as credit assignment. A common solution to this problem is to use an eligibility trace to assign credit to recency-weighted set of experienced events. However, in many realistic tasks, the set of recently experienced events are only one of the many possible action events that could have preceded the current outcome. This suggests that reinforcement learning can be made more efficient by allowing credit assignment to any viable preceding state, rather than only those most recently experienced. Accordingly, we examine ``Predecessor Features'', the fully bootstrapped version of van Hasselt's ``Expected Trace'', an algorithm that achieves this richer form of credit assignment. By maintaining a representation that approximates the expected sum of past occupancies, this algorithm allows temporal difference (TD) errors to be propagated accurately to a larger number of predecessor states than conventional methods, greatly improving learning speed. The algorithm can also be naturally extended from tabular state representation to feature representations allowing for increased performance on a wide range of environments. We demonstrate several use cases for Predecessor Features and compare its performance with other approaches.