Designing Rewards for Fast Learning
- URL: http://arxiv.org/abs/2205.15400v1
- Date: Mon, 30 May 2022 19:48:52 GMT
- Title: Designing Rewards for Fast Learning
- Authors: Henry Sowerby, Zhiyuan Zhou, Michael L. Littman
- Abstract summary: We look at how reward-design choices impact learning speed and seek to identify principles of good reward design that quickly induce target behavior.
We propose a linear-programming based algorithm that efficiently finds a reward function that maximizes action gap and minimizes subjective discount.
- Score: 18.032654606016447
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: To convey desired behavior to a Reinforcement Learning (RL) agent, a designer
must choose a reward function for the environment, arguably the most important
knob designers have in interacting with RL agents. Although many reward
functions induce the same optimal behavior (Ng et al., 1999), in practice, some
of them result in faster learning than others. In this paper, we look at how
reward-design choices impact learning speed and seek to identify principles of
good reward design that quickly induce target behavior. This
reward-identification problem is framed as an optimization problem: Firstly, we
advocate choosing state-based rewards that maximize the action gap, making
optimal actions easy to distinguish from suboptimal ones. Secondly, we propose
minimizing a measure of the horizon, something we call the "subjective
discount", over which rewards need to be optimized to encourage agents to make
optimal decisions with less lookahead. To solve this optimization problem, we
propose a linear-programming based algorithm that efficiently finds a reward
function that maximizes action gap and minimizes subjective discount. We test
the rewards generated with the algorithm in tabular environments with
Q-Learning, and empirically show they lead to faster learning. Although we only
focus on Q-Learning because it is perhaps the simplest and most well understood
RL algorithm, preliminary results with R-max (Brafman and Tennenholtz, 2000)
suggest our results are much more general. Our experiments support three
principles of reward design: 1) consistent with existing results, penalizing
each step taken induces faster learning than rewarding the goal. 2) When
rewarding subgoals along the target trajectory, rewards should gradually
increase as the goal gets closer. 3) Dense reward that's nonzero on every state
is only good if designed carefully.
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