Diverse Transformer Decoding for Offline Reinforcement Learning Using Financial Algorithmic Approaches

TL;DR

This paper introduces Portfolio Beam Search, a novel decoding method for offline reinforcement learning with Transformers, inspired by financial algorithms, which enhances exploration and reduces variability in decision sequences.

Contribution

It proposes Portfolio Beam Search, an uncertainty-aware diversification technique that improves exploration and stability in offline RL decoding with Transformers.

Findings

Higher returns on D4RL locomotion benchmark

Significant reduction in outcome variability

Enhanced exploration during decoding

Abstract

Offline Reinforcement Learning (RL) algorithms learn a policy using a fixed training dataset, which is then deployed online to interact with the environment and make decisions. Transformers, a standard choice for modeling time-series data, are gaining popularity in offline RL. In this context, Beam Search (BS), an approximate inference algorithm, is the go-to decoding method. Offline RL eliminates the need for costly or risky online data collection. However, the restricted dataset induces uncertainty as the agent may encounter unfamiliar sequences of states and actions during execution that were not covered in the training data. In this context, BS lacks two important properties essential for offline RL: It does not account for the aforementioned uncertainty, and its greedy left-right search approach often results in sequences with minimal variations, failing to explore potentially better alternatives. To address these limitations, we propose Portfolio Beam Search (PBS), a simple-yet-effective alternative to BS that balances exploration and exploitation within a Transformer model during decoding. We draw inspiration from financial economics and apply these principles to develop an uncertainty-aware diversification mechanism, which we integrate into a sequential decoding algorithm at inference time. We empirically demonstrate the effectiveness of PBS on the D4RL locomotion benchmark, where it achieves higher returns and significantly reduces outcome variability.