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General Policy Evaluation and Improvement by Learning to Identify Few But Crucial States by Faccio, Francesco; Ramesh, Aditya; Herrmann, Vincent; Harb, Jean; Schmidhuber, Jürgen is a scholarly article available to read on EtoBox.

What is General Policy Evaluation and Improvement by Learning to Identify Few But Crucial States about?

Learning to evaluate and improve policies is a core problem of Reinforcement Learning (RL). Traditional RL algorithms learn a value function defined for a single policy. A recently explored competitive alternative is to learn a single value function for many policies. Here we combine the actor-critic architecture of Parameter-Based Value Functions and the policy embedding of Policy Evaluation Networks to learn a single value function for evaluating (and thus helping to improve) any policy represented by a deep neural network (NN). The method yields competitive experimental results. In continuous control problems with infinitely many states, our value function minimizes its prediction error by simultaneously learning a small set of `probing states' and a mapping from actions produced in probing states to the policy's return. The method extracts crucial abstract knowledge about the environment in form of very few states sufficient to fully specify the behavior of many policies. A policy improves solely by changing actions in probing states, following the gradient of the value function's predictions. Surprisingly, it is possible to clone the behavior of a near-optimal policy in Swimme

Author

Faccio, Francesco; Ramesh, Aditya; Herrmann, Vincent; Harb, Jean; Schmidhuber, Jürgen

Published

2022

Language

EN