Tackling Long-Horizon Tasks with Model-based Offline Reinforcement Learning

• URL: http://arxiv.org/abs/2407.00699v1
• Date: Sun, 30 Jun 2024 13:44:59 GMT
• Title: Tackling Long-Horizon Tasks with Model-based Offline Reinforcement Learning
• Authors: Kwanyoung Park, Youngwoon Lee,
• Abstract summary: We introduce a novel model-based offline RL method, Lower Expectile Q-learning (LEQ), which enhances long-horizon task performance. Our empirical results show that LEQ significantly outperforms previous model-based offline RL methods on long-horizon tasks. LEQ achieves performance comparable to the state-of-the-art model-based and model-free offline RL methods on the NeoRL benchmark and the D4RL MuJoCo Gym tasks.
• Score: 6.345851712811528
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Model-based offline reinforcement learning (RL) is a compelling approach that addresses the challenge of learning from limited, static data by generating imaginary trajectories using learned models. However, it falls short in solving long-horizon tasks due to high bias in value estimation from model rollouts. In this paper, we introduce a novel model-based offline RL method, Lower Expectile Q-learning (LEQ), which enhances long-horizon task performance by mitigating the high bias in model-based value estimation via expectile regression of $\lambda$-returns. Our empirical results show that LEQ significantly outperforms previous model-based offline RL methods on long-horizon tasks, such as the D4RL AntMaze tasks, matching or surpassing the performance of model-free approaches. Our experiments demonstrate that expectile regression, $\lambda$-returns, and critic training on offline data are all crucial for addressing long-horizon tasks. Additionally, LEQ achieves performance comparable to the state-of-the-art model-based and model-free offline RL methods on the NeoRL benchmark and the D4RL MuJoCo Gym tasks.

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