Imitation Learning via Off-Policy Distribution Matching (1912.05032v1)

Abstract: When performing imitation learning from expert demonstrations, distribution matching is a popular approach, in which one alternates between estimating distribution ratios and then using these ratios as rewards in a standard reinforcement learning (RL) algorithm. Traditionally, estimation of the distribution ratio requires on-policy data, which has caused previous work to either be exorbitantly data-inefficient or alter the original objective in a manner that can drastically change its optimum. In this work, we show how the original distribution ratio estimation objective may be transformed in a principled manner to yield a completely off-policy objective. In addition to the data-efficiency that this provides, we are able to show that this objective also renders the use of a separate RL optimization unnecessary.Rather, an imitation policy may be learned directly from this objective without the use of explicit rewards. We call the resulting algorithm ValueDICE and evaluate it on a suite of popular imitation learning benchmarks, finding that it can achieve state-of-the-art sample efficiency and performance.

• The paper presents ValueDICE, a novel off-policy algorithm that directly optimizes policies by matching expert and learner distributions.
• ValueDICE leverages a modified Donsker-Varadhan objective and a Bellman operator to significantly reduce sample requirements.
• Experimental results in MuJoCo benchmarks show ValueDICE outperforms traditional methods, highlighting its practical viability in data-scarce settings.

Analysis of "Imitation Learning via Off-Policy Distribution Matching"

The paper "Imitation Learning via Off-Policy Distribution Matching" presents a novel approach to address the sample inefficiency problem in imitation learning (IL) by introducing ValueDICE, an algorithm that leverages off-policy methods to match distributions between expert and learned policies. This paper, authored by Ilya Kostrikov et al., contributes significantly to the field by providing a means of performing imitation learning that is both sample-efficient and requires no additional reward learning.

The core problem tackled by this research arises in scenarios where explicit reward structures are either unavailable or cumbersome to define. Traditionally, imitation learning involves duplicating behaviors from expert demonstrations, typically using distribution matching techniques such as Adversarial Imitation Learning (AIL). AIL optimizes the policy by estimating the distribution divergences between the expert and the model's state-action distributions. However, these methods often depend on "on-policy" data, requiring fresh samples for each policy update, leading to cost prohibitive iterations in environments with limited accessibility.

ValueDICE is introduced as a solution to this limitation. It transforms the conventional distribution ratio estimation objectives, employing the Donsker-Varadhan representation of the KL-divergence, to enable off-policy learning. This transformation allows the use of past data, significantly improving sample efficiency. The derived ValueDICE objective integrates a variant of the BeLLMan operator to maintain an off-policy framework, illustrating that it is possible to minimize divergence without explicitly observing on-policy samples.

A noteworthy aspect of this work is its ability to eliminate the need for a separate reinforcement learning optimization phase, typically used to maximize cumulative rewards derived from estimated distribution ratios. Instead, ValueDICE directly optimizes the policy using the gradients of the off-policy distribution matching objective, simplifying the learning process and reducing computational overhead.

Experimental results demonstrate the efficacy of ValueDICE. The algorithm matched or surpassed state-of-the-art methods' performance in various benchmarks, including complex environments simulated with MuJoCo, using significantly fewer environment interactions. This progression is particularly evident in scenarios involving limited expert demonstrations where traditional techniques like Behavioral Cloning (BC) often fail due to distributional drift. Moreover, ValueDICE performs strongly even with a minimal number of expert trajectories, a significant advantage in real-world applications where acquiring such data is costly or infeasible.

The implications of this research are multifaceted. Practically, ValueDICE offers a framework for imitation learning that is more viable in situations with restricted access to learning environments, potentially broadening IL's impacts across numerous domains, such as autonomous systems, robotics, and healthcare. Theoretically, it bridges a crucial gap between on-policy heavy algorithms and off-the-shelf data-efficient learning methods, prompting further exploration of off-policy techniques in distribution matching contexts.

As the field progresses, future developments may refine these methodologies by enhancing the stability and range of function approximations used in such objectives. Additionally, extending these approaches to multi-agent systems or environments with dynamic reward structures could be extremely promising. Nevertheless, ValueDICE marks a significant step forward in creating more adaptable and efficient imitation learning frameworks.