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Arxiv
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TROFI: Trajectory-Ranked Offline Inverse Reinforcement Learning
- TROFI is a new approach in offline reinforcement learning that aims to train agents without a predefined reward function.
- It first learns a reward function from human preferences to label the dataset, enabling training of the policy.
- Experiments on the D4RL benchmark show that TROFI outperforms baselines and performs similarly to using the ground truth reward.
- The efficacy of TROFI is validated in a 3D game environment, emphasizing the importance of a well-engineered reward function in reinforcement learning.
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