Active Exploration for Inverse Reinforcement Learning (2207.08645v4)

Abstract: Inverse Reinforcement Learning (IRL) is a powerful paradigm for inferring a reward function from expert demonstrations. Many IRL algorithms require a known transition model and sometimes even a known expert policy, or they at least require access to a generative model. However, these assumptions are too strong for many real-world applications, where the environment can be accessed only through sequential interaction. We propose a novel IRL algorithm: Active exploration for Inverse Reinforcement Learning (AceIRL), which actively explores an unknown environment and expert policy to quickly learn the expert's reward function and identify a good policy. AceIRL uses previous observations to construct confidence intervals that capture plausible reward functions and find exploration policies that focus on the most informative regions of the environment. AceIRL is the first approach to active IRL with sample-complexity bounds that does not require a generative model of the environment. AceIRL matches the sample complexity of active IRL with a generative model in the worst case. Additionally, we establish a problem-dependent bound that relates the sample complexity of AceIRL to the suboptimality gap of a given IRL problem. We empirically evaluate AceIRL in simulations and find that it significantly outperforms more naive exploration strategies.

• The paper introduces AceIRL, a novel algorithm that builds confidence intervals over reward functions to guide active exploration in IRL.
• It proposes two strategies—greedy and full optimization—to minimize reward uncertainty without relying on a generative model.
• Empirical results indicate AceIRL outperforms random exploration and rivals model-based methods, achieving rapid convergence in challenging environments.

Analyzing Active Exploration for Inverse Reinforcement Learning

The paper "Active Exploration for Inverse Reinforcement Learning" by Lindner et al. presents a novel algorithm, AceIRL, designed to enhance exploration efficiency in Inverse Reinforcement Learning (IRL). The development of AceIRL addresses prevalent limitations in existing IRL approaches, particularly in scenarios where the transition model and the expert policy are unknown, and the environment is accessed solely through interactions.

Summary of the Research and Methodology

Inverse Reinforcement Learning focuses on deducing a reward function from expert demonstrations, bypassing the need for an explicitly defined reward structure. Traditional IRL methods often assume access to a generative model, which is not feasible in many practical applications. AceIRL innovates by proposing an exploration strategy that does not require a generative model while achieving sample complexity comparable to methods that do.

AceIRL constructs confidence intervals around plausible reward functions based on observed interactions, guiding exploration towards informative regions of the state space. The algorithm operates over episodes, updating estimates of the environment dynamics and expert policy iteratively. An essential contribution of this work is the theoretical formulation of AceIRL's sample complexity bounds, which are framed without reliance on generative models—a departure from prior work.

Key Contributions and Findings

1. Problem Definition and Theoretical Foundations: The authors lay a formal groundwork for active IRL in finite-horizon Markov Decision Processes (MDPs), detailing the necessary and sufficient conditions for solving such problems. They extend existing analyses of estimation errors from transition models and expert policies to finite-horizon settings, connecting these errors to policy performance.
2. Algorithmic Innovation: AceIRL introduces two exploration strategies—one based on a simple greedy policy concerning reward uncertainty ("AceIRL Greedy") and another that considers expected reductions in uncertainty ("AceIRL Full"). The full version optimizes exploration by solving a convex optimization problem to select policies that minimize the predicted uncertainty at future iterations.
3. Empirical Evaluation: The paper's empirical results demonstrate that AceIRL outperforms naive exploration strategies like random exploration and even competitive with generative model-based algorithms like TRAVEL, particularly when sampled efficiently using small batch sizes for exploration. Through experimentation on environments such as "Four Paths" and "Double Chain," AceIRL consistently led to more rapid convergence to the optimal policy under the learned reward function.
4. Sample Complexity Analysis: AceIRL's sample complexity is proven to match that of techniques relying on generative models in a worst-case scenario. Additionally, it presents a problem-dependent complexity bound linked to the advantage function, allowing superior performance in environments with distinct suboptimality gaps.

Implications and Future Research

The implications of this research are multifaceted. Practically, it extends the applicability of IRL to real-world scenarios where assumptions about complete model knowledge are untenable. Theoretically, it bridges a gap in the IRL literature by ensuring sample efficiency without generative models. The dual exploration strategies present a compelling case for adaptable algorithms capable of efficient learning in diverse environments.

However, further investigations can explore extending AceIRL to continuous state and action spaces, enabling applications in more complex environments. The challenge of reducing computational demands, especially in solving convex optimization problems at each iteration, offers another avenue for future refinement.

Overall, AceIRL represents a significant stride towards making IRL more applicable and efficient, paving the way for robust learning applications in uncertain and dynamic environments. This paper contributes critically to the landscape of reinforcement learning by advocating for active exploration as a means to enhance the effectiveness and applicability of IRL methodologies.