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Preference-based reinforcement learning (RL) provides a framework to trainagents using human preferences between two behaviors. However, preference-basedRL has been challenging to scale since it requires a large amount of humanfeedback to learn a reward function aligned with human intent. In this paper,we present Preference Transformer, a neural architecture that models humanpreferences using transformers. Unlike prior approaches assuming human judgmentis based on the Markovian rewards which contribute to the decision equally, weintroduce a new preference model based on the weighted sum of non-Markovianrewards. We then design the proposed preference model using a transformerarchitecture that stacks causal and bidirectional self-attention layers. Wedemonstrate that Preference Transformer can solve a variety of control tasksusing real human preferences, while prior approaches fail to work. We also showthat Preference Transformer can induce a well-specified reward and attend tocritical events in the trajectory by automatically capturing the temporaldependencies in human decision-making. Code is available on the projectwebsite: https://sites.google.com/view/preference-transformer.

Preference Transformer: Modeling Human Preferences using Transformers for RL