Title: Reward Inflation Paradigm Through the Lens of Monetary Economics
Primary area: reinforcement learning
Keywords: reinforcement learning, reward inflation, money supply, monetary economics, economic growth, monetary policy

Abstract: Reward is fundamental to reinforcement learning (RL), where the agent treats it as an incentive to maximize.
This appearance is akin to a rational human who maximizes their income.
However, in the real economy, money expands to stimulate economic growth.
Inspired by this principle of monetary economics, we introduce a novel RL paradigm, reward inflation, which gradually increases the reward scale during training.
Analogous to inflationary policies used by central banks to stimulate economic growth, reward inflation acts as an incentive stimulus for agents to accelerate policy learning.
Reward inflation can be applied in two ways: fixed or adaptive.
Motivated by the Fed's monetary policy, we propose FedeRL, a dynamic controller for adaptive inflation.
Theoretical analysis suggests that the effect of reward inflation is threefold: (1) induces recency bias in temporal-difference learning, (2) amplifies policy gradients, and (3) enhances neural activation.
Empirical results corroborate these insights, showing that moderate inflation improves performance on continuous control tasks.
Moreover, FedeRL performed even better than fixed inflation and outperformed comparable baselines.
By translating economic growth principles into RL, our approach offers a novel perspective that strengthens policy optimization and addresses fundamental RL objectives.
The implementation code will be made publicly available.