Improving Intrinsic Exploration by Creating Stationary Objectives
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Keywords: Reinforcement Learning, Exploration, Intrinsic Rewards, Stationarity
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TL;DR: We propose a framework to transform intrinsic reward methods into stationary learning signals, which enables better policy learning across many challenging environments.
Abstract: Exploration bonuses in reinforcement learning guide long-horizon exploration by defining custom intrinsic objectives. Count-based methods use the frequency of state visits to derive an exploration bonus. In this paper, we identify that any intrinsic reward function derived from count-based methods is non-stationary and hence induces a difficult objective to optimize for the agent. The key contribution of our work lies in transforming the original non-stationary rewards into stationary rewards through an augmented state representation. For this purpose, we introduce the Stationary Objectives For Exploration (SOFE) framework. SOFE requires *identifying* sufficient statistics for different exploration bonuses and finding an *efficient* encoding of these statistics to use as input to a deep network. SOFE is based on proposing state augmentations that expand the state space but hold the promise of simplifying the optimization of the agent's objective. Our experiments show that SOFE improves the agents' performance in challenging exploration problems, including sparse-reward tasks, pixel-based observations, 3D navigation, and procedurally generated environments.
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Primary Area: reinforcement learning
Submission Number: 4213