Zhang, Y., Zhou, W., & Zhou, Y. (2024). On Reward Transferability in Adversarial Inverse Reinforcement Learning: Insights from Random Matrix Theory and Unobservable State Transitions (arXiv:2410.07643v1). arXiv. https://doi.org/10.48550/arXiv.2410.07643
This paper investigates the reward transferability in Adversarial Inverse Reinforcement Learning (AIRL) when the state transition matrix is unobservable, challenging the prevailing belief that the decomposability condition is the primary factor influencing transfer effectiveness.
The authors employ Random Matrix Theory (RMT) to analyze the transferability condition in AIRL with an unobservable transition matrix, modeled using a variational inference approach with a flat Dirichlet prior. They then extend this analysis to scenarios with informative priors, where specific elements of the transition matrix are known. The paper further examines the impact of on-policy and off-policy RL algorithms on reward extraction and proposes a hybrid framework, PPO-AIRL + SAC, combining the strengths of both approaches.
The paper concludes that the effectiveness of reward transfer in AIRL is primarily determined by the choice of RL algorithm, advocating for on-policy methods during reward extraction and off-policy methods during policy re-optimization. The proposed hybrid framework, PPO-AIRL + SAC, effectively leverages the strengths of both approaches, leading to improved reward transfer performance.
This research provides valuable insights into the factors influencing reward transferability in AIRL, particularly in practical scenarios with unobservable state transitions. The findings challenge existing assumptions and offer a novel perspective on optimizing AIRL for effective transfer learning.
The paper primarily focuses on theoretical analysis and simulations. Further empirical validation on a wider range of complex tasks and environments is necessary to solidify the findings. Investigating the impact of different prior distributions on the transition matrix and exploring alternative hybrid frameworks could be promising avenues for future research.
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by Yangchun Zha... klokken arxiv.org 10-11-2024
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