Parameter-Based Value Functions

Francesco Faccio, Louis Kirsch, Jürgen Schmidhuber

Keywords: Reinforcement Learning, Off-Policy Reinforcement Learning

Abstract: Traditional off-policy actor-critic Reinforcement Learning (RL) algorithms learn value functions of a single target policy. However, when value functions are updated to track the learned policy, they forget potentially useful information about old policies. We introduce a class of value functions called Parameter-Based Value Functions (PBVFs) whose inputs include the policy parameters. They can generalize across different policies. PBVFs can evaluate the performance of any policy given a state, a state-action pair, or a distribution over the RL agent's initial states. First we show how PBVFs yield novel off-policy policy gradient theorems. Then we derive off-policy actor-critic algorithms based on PBVFs trained by Monte Carlo or Temporal Difference methods. We show how learned PBVFs can zero-shot learn new policies that outperform any policy seen during training. Finally our algorithms are evaluated on a selection of discrete and continuous control tasks using shallow policies and deep neural networks. Their performance is comparable to state-of-the-art methods.

One-sentence Summary: We propose value functions whose inputs include the policy parameters and which can generalize across different policies

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Code: [ ff93/parameter-based-value-functions](https://github.com/ff93/parameter-based-value-functions)

Community Implementations: [ 1 code implementation](https://www.catalyzex.com/paper/arxiv:2006.09226/code)