Offline Inverse Reinforcement Learning: Maximizing Likelihood for Expert Behavior Recovery

Core Concepts

The author proposes a bi-level optimization approach to estimate rewards accurately from expert demonstrations, addressing challenges in offline IRL. The algorithm outperforms existing benchmarks by recovering high-quality reward functions.

Abstract

The content discusses Offline Inverse Reinforcement Learning (IRL) and proposes a new algorithmic framework to recover reward structures accurately from expert demonstrations. By incorporating conservatism into the model-based setting, the proposed method aims to maximize likelihood over observed expert trajectories. Extensive experiments demonstrate that the algorithm surpasses state-of-the-art methods in various robotics control tasks. The theoretical analysis provides guarantees of performance for the recovered reward estimator, showcasing its effectiveness in practical applications.

Stats

"We propose a new algorithmic framework to solve the bi-level optimization problem formulation and provide statistical and computational guarantees of performance."
"Finally, we demonstrate that the proposed algorithm outperforms the state-of-the-art offline IRL and imitation learning benchmarks by a large margin."
"Our main contributions are listed as follows: Maximum Likelihood Estimation, Transition Samples, World Model, Offline IRL, Expert Trajectories, Reward Estimator."
"In extensive experiments using robotic control tasks in MuJoCo and collected datasets in D4RL benchmark."
"We show that the proposed algorithm outperforms existing benchmarks significantly."

Quotes

"We propose a two-stage procedure to estimate dynamics models and recover optimal policies based on maximum likelihood estimation."
"Our algorithm demonstrates superior performance compared to existing offline IRL methods."
"Theoretical guarantees ensure accurate recovery of reward functions from limited expert demonstrations."

Key Insights Distilled From

When Demonstrations Meet Generative World Models

by Siliang Zeng... at arxiv.org 03-01-2024

https://arxiv.org/pdf/2302.07457.pdf

Deeper Inquiries

How can incorporating conservatism improve reward estimation accuracy beyond traditional methods

Incorporating conservatism in reward estimation can improve accuracy by addressing the issue of distribution shift. Traditional methods may struggle with generalizing well to new, unseen states and actions in the real environment due to this shift. By introducing a penalty function that quantifies model uncertainty and regularizes the reward estimator, conservative models can avoid risky exploration in unfamiliar regions of the state-action space where data coverage is limited. This approach helps mitigate inaccuracies caused by incomplete or biased datasets, leading to more robust and accurate reward estimations.

What are potential limitations or biases introduced by relying solely on expert demonstrations for reward learning

Relying solely on expert demonstrations for reward learning introduces potential limitations and biases. One limitation is that expert demonstrations may not cover all possible scenarios or variations in the environment, resulting in a limited understanding of optimal behavior across diverse conditions. Biases can arise if experts have preferences or behaviors that are not truly optimal but are still reflected in their demonstrations. Additionally, relying only on expert data may overlook valuable insights from other sources such as human feedback or additional metrics that could enhance the quality of learned rewards.

How can this research impact real-world applications beyond autonomous driving and clinical decision-making

This research has significant implications beyond autonomous driving and clinical decision-making applications. The development of offline Inverse Reinforcement Learning (IRL) algorithms opens up possibilities for various fields such as robotics, dialogue systems, gaming AI, personalized recommendation systems, financial trading strategies, and more. By accurately recovering reward functions from historical datasets without online interactions with environments or experts, these algorithms can be applied to optimize decision-making processes across a wide range of industries where learning from past experiences is crucial for success.