Leveraging Additional Information in POMDPs with Guided Policy Optimization

ICLR 2025 Conference Submission4442 Authors

Keywords: Reinforcement Learning, Imitation Learning, POMDP, policy gradient

Abstract:

Reinforcement Learning (RL) in partially observable environments poses significant challenges due to the complexity of learning under uncertainty.
While additional information, such as that available in simulations, can enhance training, effectively leveraging it remains an open problem.
To address this, we introduce Guided Policy Optimization (GPO), a framework that co-trains a guider and a learner.
The guider takes advantage of supplementary information while ensuring alignment with the learner's policy, which is primarily trained via Imitation Learning (IL).
We theoretically demonstrate that this learning scheme achieves optimality comparable to direct RL, thereby overcoming key limitations inherent in IL approaches.
Our approach includes two practical variants, GPO-penalty and GPO-clip, and empirical evaluations show strong performance across various tasks, including continuous control with partial observability and noise, and memory-based challenges, significantly outperforming existing methods.

Primary Area: reinforcement learning

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Submission Number: 4442