Model-Based Reward Shaping for Adversarial Inverse Reinforcement Learning in Stochastic Environments
- URL: http://arxiv.org/abs/2410.03847v1
- Date: Fri, 4 Oct 2024 18:27:37 GMT
- Title: Model-Based Reward Shaping for Adversarial Inverse Reinforcement Learning in Stochastic Environments
- Authors: Simon Sinong Zhan, Qingyuan Wu, Philip Wang, Yixuan Wang, Ruochen Jiao, Chao Huang, Qi Zhu,
- Abstract summary: We tackle the limitation of the Adrial Inverse Reinforcement Learning (AIRL) method in environments where theoretical results cannot hold and performance is degraded.
We propose a novel method which infuses the dynamics information into the reward shaping with the theoretical guarantee for the induced optimal policy in the environments.
We present a novel Model-Enhanced AIRL framework, which integrates transition model estimation directly into reward shaping.
- Score: 11.088387316161064
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: In this paper, we aim to tackle the limitation of the Adversarial Inverse Reinforcement Learning (AIRL) method in stochastic environments where theoretical results cannot hold and performance is degraded. To address this issue, we propose a novel method which infuses the dynamics information into the reward shaping with the theoretical guarantee for the induced optimal policy in the stochastic environments. Incorporating our novel model-enhanced rewards, we present a novel Model-Enhanced AIRL framework, which integrates transition model estimation directly into reward shaping. Furthermore, we provide a comprehensive theoretical analysis of the reward error bound and performance difference bound for our method. The experimental results in MuJoCo benchmarks show that our method can achieve superior performance in stochastic environments and competitive performance in deterministic environments, with significant improvement in sample efficiency, compared to existing baselines.
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