Model-Based Reward Shaping for Adversarial Inverse Reinforcement Learning in Stochastic Environments
<|reference_start|>Model-Based Reward Shaping for Adversarial Inverse Reinforcement Learning in Stochastic Environments: 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.<|reference_end|>
@article{zhan2024model-based,
    title={Model-Based Reward Shaping for Adversarial Inverse Reinforcement
  Learning in Stochastic Environments},
    author={Simon Sinong Zhan, Qingyuan Wu, Philip Wang, Yixuan Wang, Ruochen
  Jiao, Chao Huang, Qi Zhu},
    journal={arXiv preprint arXiv:2410.03847},
    year={2024},
    archivePrefix={arXiv},
    eprint={2410.03847},
    primaryClass={cs.LG cs.AI}
}