Multi-View Reinforcement Learning
This work addresses a domain-specific problem for reinforcement learning agents in multi-view settings, presenting an incremental advancement.
The paper tackles the problem of decision-making in environments where agents share dynamics but have different observation models by introducing the Multi-View Reinforcement Learning (MVRL) framework, extending POMDPs to support multiple observation models, and demonstrates effectiveness with reductions in sample complexities and computational time.
This paper is concerned with multi-view reinforcement learning (MVRL), which allows for decision making when agents share common dynamics but adhere to different observation models. We define the MVRL framework by extending partially observable Markov decision processes (POMDPs) to support more than one observation model and propose two solution methods through observation augmentation and cross-view policy transfer. We empirically evaluate our method and demonstrate its effectiveness in a variety of environments. Specifically, we show reductions in sample complexities and computational time for acquiring policies that handle multi-view environments.