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洞見 - Adversarial Inverse Reinforcement Learning
Rethinking Adversarial Inverse Reinforcement Learning: Policy Imitation and Transferable Reward Recovery
Adversarial Inverse Reinforcement Learning reevaluated for policy imitation and transferable reward recovery.
Model-Enhanced Adversarial Inverse Reinforcement Learning in Stochastic Environments Using Model-Based Reward Shaping
This paper introduces a novel model-enhanced adversarial inverse reinforcement learning framework that leverages model-based reward shaping to improve performance in stochastic environments, addressing limitations of existing AIL methods in uncertain environments.
On the Transferability of Rewards in Adversarial Inverse Reinforcement Learning: Insights from Random Matrix Theory and Unobservable State Transitions
This paper argues that the effectiveness of reward transfer in Adversarial Inverse Reinforcement Learning (AIRL) is primarily influenced by the choice of the Reinforcement Learning (RL) algorithm, specifically whether it's on-policy or off-policy, rather than the previously emphasized decomposability condition.
