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Found 2 papers, 1 papers with code
Policy Distillation with Selective Input Gradient Regularization for Efficient Interpretability
Although deep Reinforcement Learning (RL) has proven successful in a wide range of tasks, one challenge it faces is interpretability when applied to real-world problems.
Domain Adaptation In Reinforcement Learning Via Latent Unified State Representation
To address this issue, we propose a two-stage RL agent that first learns a latent unified state representation (LUSR) which is consistent across multiple domains in the first stage, and then do RL training in one source domain based on LUSR in the second stage.
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