Concetti Chiave
This paper introduces a novel model-enhanced adversarial inverse reinforcement learning framework that leverages model-based reward shaping to improve performance in stochastic environments, addressing limitations of existing AIL methods in uncertain environments.
Sintesi
Bibliographic Information:
Zhan, S. S., Wu, Q., Wang, P., Wang, Y., Jiao, R., Huang, C., & Zhu, Q. (2024). Model-Based Reward Shaping for Adversarial Inverse Reinforcement Learning in Stochastic Environments. arXiv preprint arXiv:2410.03847.
Research Objective:
This paper aims to address the limitations of Adversarial Inverse Reinforcement Learning (AIRL) in stochastic environments, where existing methods struggle due to the deterministic nature of their reward formulations.
Methodology:
The authors propose a novel model-enhanced AIRL framework that incorporates:
- Model-Based Reward Shaping: Instead of relying solely on state-action pairs or single-step transitions, the framework utilizes an estimated transition model to shape rewards, considering the stochasticity of the environment.
- Adversarial Reward Learning: Inspired by Generative Adversarial Networks (GANs), the framework trains a discriminator to distinguish between expert demonstrations and agent-generated trajectories, guiding the reward learning process.
- Model-Based Trajectory Generation: The learned transition model is used to generate synthetic trajectories, improving sample efficiency by reducing the reliance on costly real-world interactions.
Key Findings:
- The proposed model-enhanced reward shaping method guarantees policy invariance, ensuring that the learned policy's optimal behavior aligns with the ground-truth reward function.
- Theoretical analysis establishes bounds on both the reward function error and the performance difference, demonstrating that these errors decrease as the accuracy of the learned transition model improves.
- Empirical evaluations on MuJoCo benchmark environments demonstrate the framework's superiority in stochastic environments, achieving better performance and sample efficiency compared to existing AIL methods.
- The framework also maintains competitive performance in deterministic environments, highlighting its robustness and generalizability.
Main Conclusions:
The proposed model-enhanced adversarial IRL framework effectively addresses the challenges of learning from demonstrations in stochastic environments. By incorporating model-based techniques and reward shaping, the framework learns robust reward functions and policies, leading to improved performance and sample efficiency.
Significance:
This research significantly contributes to the field of imitation learning by providing a practical and theoretically grounded solution for learning in uncertain environments. The framework's ability to handle stochasticity broadens the applicability of IRL to real-world scenarios where deterministic assumptions do not hold.
Limitations and Future Research:
- The paper primarily focuses on single-agent reinforcement learning settings. Exploring extensions to multi-agent and hierarchical scenarios could further enhance the framework's applicability.
- Investigating the generalization ability of the framework in transfer learning tasks would be a valuable direction for future research.
Statistiche
The authors trained their algorithm for 100k environmental steps for InvertedPendulum-v4 and InvertedDoublePendulum-v4, and 1M steps for Hopper-v3.
They evaluated the performance every 1k steps for InvertedPendulum-v4 and InvertedDoublePendulum-v4, and every 10k steps for Hopper-v3.
The experiments were conducted using 5 different random seeds to ensure robustness and generalizability of the results.
To simulate stochastic dynamics in MuJoCo, Gaussian noise with a mean of 0 and a standard deviation of 0.5 was introduced to the environmental interaction steps.
Citazioni
"The challenge in stochastic environments calls for a different perspective of rewards – stochastic rewards absorbing the transition information."
"To our knowledge, this is the first study that provides a theoretical analysis on the performance difference with a learned dynamic model for the adversarial IRL problem under stochastic MDP."
"Our method shows significant superiority in sample efficiency across all of the benchmarks under both deterministic and stochastic settings."