A Survey of Inverse Reinforcement Learning: Challenges, Methods and Progress (1806.06877v3)

Abstract: Inverse reinforcement learning (IRL) is the problem of inferring the reward function of an agent, given its policy or observed behavior. Analogous to RL, IRL is perceived both as a problem and as a class of methods. By categorically surveying the current literature in IRL, this article serves as a reference for researchers and practitioners of machine learning and beyond to understand the challenges of IRL and select the approaches best suited for the problem on hand. The survey formally introduces the IRL problem along with its central challenges such as the difficulty in performing accurate inference and its generalizability, its sensitivity to prior knowledge, and the disproportionate growth in solution complexity with problem size. The article elaborates how the current methods mitigate these challenges. We further discuss the extensions to traditional IRL methods for handling: inaccurate and incomplete perception, an incomplete model, multiple reward functions, and nonlinear reward functions. This survey concludes the discussion with some broad advances in the research area and currently open research questions.

Overview of Inverse Reinforcement Learning: Challenges, Methods, and Progress

This paper presents a thorough survey of inverse reinforcement learning (IRL), a methodology that involves deducing an agent's reward function based on its observed behavior or policy. Unlike reinforcement learning (RL), which focuses on discovering an optimal policy given a predefined reward structure, IRL aims to infer the hidden reward function that results in the observed behavior.

Challenges in IRL

The survey extensively discusses four primary challenges intrinsic to IRL:

1. Accurate Inference: A significant obstacle is the ambiguity and degeneracy of solutions. Multiple reward functions can often explain the same observed behavior, leading to an ill-posed optimization problem.
2. Generalizability: This entails the learned reward function's capacity to extrapolate appropriately to unobserved states and actions. Ensuring that the inferred model generalizes well is vital for effective application.
3. Sensitivity to Prior Knowledge: The accuracy of IRL is highly sensitive to the features used to describe the reward function and the correctness of the environment model (transition dynamics).
4. Solution Complexity: Both computational and sample complexity pose substantial challenges. The iterative nature of many IRL algorithms involves solving MDPs repeatedly, which can be computationally expensive.

Foundational Methods

This paper categorically reviews foundational methods of IRL, which are primarily organized into:

• Margin Optimization: These methods, such as Maximum Margin Planning (MMP) and its derivatives, focus on optimizing the margin between observed and alternative actions or policies. They contribute by offering provable convergence and formal bounds on sample complexity.
• Entropy Optimization: Techniques like Maximum Entropy IRL (MaxEnt IRL) aim to maximize the entropy of the policy or trajectory distributions, addressing the solution ambiguity challenge by favoring less biased distributions.
• Bayesian Approaches: Bayesian IRL methods update a prior distribution over reward functions using the observed data, capturing uncertainty in the reward hypothesis.
• Classification and Regression: Traditional machine learning approaches are adapted to treat state-action pairs as labeled data, contributing to robust methods like Structured Classification for IRL (SCIRL).

Extensions and Real-World Applications

The paper also sheds light on extensions to basic IRL that accommodate real-world complexities:

• Imperfect Observations: Extensions that handle noise and incomplete information in observed trajectories.
• Multiple Tasks: Methods that infer multiple reward functions from demonstrations that might involve different tasks or intentions.
• Incomplete Models: Techniques to deal with unknown or partially known transition models and incomplete feature sets.
• Nonlinear Reward Representations: Addressing scenarios where a linear approximation of the reward function is insufficient, more complex models including neural networks are employed.

Implications and Future Directions

The surveyed advancements in IRL contribute significantly to developing autonomous systems capable of learning from human demonstrations without explicit programming of the reward functions. This has practical implications across various domains, from robotics to autonomous vehicles.

The paper highlights several avenues for future research, such as improving scalability to handle high-dimensional state spaces, developing standard benchmarks for consistent evaluation of IRL methods, and enhancing theoretical understanding around sample and computational complexity. Another area of interest includes exploring the synergies between direct reward learning and indirect policy matching techniques to balance performance and generalization efficiently.

In essence, the survey provides a comprehensive roadmap for navigating the landscape of IRL research, offering insights into the diverse methods and their respective contributions toward overcoming their inherent challenges.