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Found 2 papers, 1 papers with code
Advantage-Aware Policy Optimization for Offline Reinforcement Learning
Offline Reinforcement Learning (RL) endeavors to leverage offline datasets to craft effective agent policy without online interaction, which imposes proper conservative constraints with the support of behavior policies to tackle the Out-Of-Distribution (OOD) problem.
Curricular Subgoals for Inverse Reinforcement Learning
Inverse Reinforcement Learning (IRL) aims to reconstruct the reward function from expert demonstrations to facilitate policy learning, and has demonstrated its remarkable success in imitation learning.
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