schwarzer2021pretraining: Pretraining Representations for Data-Efficient Reinforcement Learning

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tags

Reinforcement Learning, Pretraining for Reinforcement Learning

source :

authors

Schwarzer, M., Rajkumar, N., Noukhovitch, M., Anand, A., Charlin, L., Hjelm, R. D., Bachman, P., …

year

2021

Main Idea

The core of this paper proposes a set of objectives claimed to be useful for pre-training across a wide range of dataset qualities. They propose three main objectives: self-predictive representations, goal-conditioned reinforcement learning, and inverse modeling. They believe this covers a wide range of of reinforcement learning centric temporal constraints. This is expected to outperform other monolithic

Objectives

• Self-predictive representations

  Self-predictive representations (Schwarzer et al. 2021)

Reason for originally reading

As I’m working on extensions to the (McLeod et al. 2021) paper and deciding to move towards a more applied side of reinforcement learning, I’ve become minorly fascinated by pre-training objectives. Pre-training objectives could lead to “foundational models” of a sort for certain domains, allowing for engineers to more easily engineer rewards to induce the desired behavior.

References