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October 20, 2025Open Access

Guided Policy Optimization under Partial Observability

Key Points

Guided Policy Optimization achieves optimality comparable to direct reinforcement learning, enhancing learning under uncertainty.
The empirical evaluations demonstrate that GPO significantly outperforms existing methods in continuous control tasks.
The framework utilizes privileged information through a guider, aligning closely with the learner's policy from imitation learning.
Challenges in partially observable environments and memory-based tasks are effectively addressed through this innovative approach.

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 privileged information while ensuring alignment with the learner's policy that is primarily trained via imitation learning. We theoretically demonstrate that this learning scheme achieves optimality comparable to direct RL, thereby overcoming key limitations inherent in existing approaches. Empirical evaluations show strong performance of GPO across various tasks, including continuous control with partial observability and noise, and memory-based challenges, significantly outperforming existing methods.

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