Swapped goal-conditioned offline reinforcement learning

• URL: http://arxiv.org/abs/2302.08865v1
• Date: Fri, 17 Feb 2023 13:22:40 GMT
• Title: Swapped goal-conditioned offline reinforcement learning
• Authors: Wenyan Yang, Huiling Wang, Dingding Cai, Joni Pajarinen, Joni-Kristen K\"am\"ar\"ainen
• Abstract summary: We present a general offline reinforcement learning method called deterministic Q-advantage policy gradient (DQAPG) In the experiments, DQAPG outperforms state-of-the-art goal-conditioned offline RL methods in a wide range of benchmark tasks.
• Score: 8.284193221280216
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Offline goal-conditioned reinforcement learning (GCRL) can be challenging due to overfitting to the given dataset. To generalize agents' skills outside the given dataset, we propose a goal-swapping procedure that generates additional trajectories. To alleviate the problem of noise and extrapolation errors, we present a general offline reinforcement learning method called deterministic Q-advantage policy gradient (DQAPG). In the experiments, DQAPG outperforms state-of-the-art goal-conditioned offline RL methods in a wide range of benchmark tasks, and goal-swapping further improves the test results. It is noteworthy, that the proposed method obtains good performance on the challenging dexterous in-hand manipulation tasks for which the prior methods failed.

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