Discriminator-Actor-Critic: Addressing Sample Inefficiency and Reward Bias in Adversarial Imitation Learning (1809.02925v2)

Abstract: We identify two issues with the family of algorithms based on the Adversarial Imitation Learning framework. The first problem is implicit bias present in the reward functions used in these algorithms. While these biases might work well for some environments, they can also lead to sub-optimal behavior in others. Secondly, even though these algorithms can learn from few expert demonstrations, they require a prohibitively large number of interactions with the environment in order to imitate the expert for many real-world applications. In order to address these issues, we propose a new algorithm called Discriminator-Actor-Critic that uses off-policy Reinforcement Learning to reduce policy-environment interaction sample complexity by an average factor of 10. Furthermore, since our reward function is designed to be unbiased, we can apply our algorithm to many problems without making any task-specific adjustments.

Analyzing the Discriminator-Actor-Critic Algorithm for Adversarial Imitation Learning

The paper presented in the paper explores the challenges and advancements within the Adversarial Imitation Learning (AIL) framework, focusing on addressing sample inefficiency and reward bias—two critical issues that commonly hinder the effectiveness of this approach in real-world applications. The paper proposes a novel algorithm known as the Discriminator-Actor-Critic (DAC) that leverages off-policy reinforcement learning to enhance the sample efficiency and mitigate reward biases that are prevalent in the existing algorithms such as Generative Adversarial Imitation Learning (GAIL) and Adversarial Inverse Reinforcement Learning (AIRL).

Key Contributions and Technical Advancements

The paper identifies and addresses significant limitations inherent in existing AIL methodologies. Firstly, it highlights the implicit bias in reward functions, which, while beneficial in certain scenarios, can lead to suboptimal policy performance when environments deviate from these biased assumptions. The DAC algorithm introduces methodological revisions to produce an unbiased reward function that exhibits increased robustness across varied environments without necessitating task-specific alterations.

1. Bias in Reward Formulations: The research critically analyses common reward formulations used in GAIL and AIRL, pointing out their intrinsic limitations when environments have either survival bonuses or per-step penalties. The GAIL reward function, for instance, may inadvertently prioritize lengthy episodic survival over task completion efficiency, skewing the learning objectives away from optimal trajectories.
2. Enhanced Sample Efficiency: The paper addresses the prohibitive number of environment interactions required by existing AIL methods by incorporating the Twin Delayed Deep Deterministic Policy Gradient (TD3) as the reinforcement learning backbone. By adopting off-policy learning and incorporating a replay buffer, DAC algorithm reduces sample complexity substantially—by an average factor of 10.
3. Unbiased Handling of Terminal States: The proposed DAC algorithm tackles the challenges associated with improperly handling terminal or absorbing states. By learning rewards for such states, DAC ensures these states don't contribute negatively to policy evaluation, aligning the learning process with intuitive task objectives more accurately.
4. State-of-the-Art Performance: The paper reports DAC's state-of-the-art performance across several standard benchmarks, achieving notable efficiency in tasks with demonstrated policy trajectories. Through various experiments, the DAC algorithm demonstrated its superiority by substantially reducing the sample complexity while maintaining or even exceeding the performance of existing approaches.

Implications and Future Directions

The implications of the advancements introduced by the DAC algorithm are both practical and theoretical. Practically, the significant reduction in sample complexity broadens the potential application of imitation learning to high-stakes domains, such as robotics, where real-world trials are costly or infeasible. Theoretically, the introduction of an unbiased reward mechanism enriches the fundamental understanding of reward function design in imitation learning paradigms.

Future research directions can build upon these contributions by exploring further enhancements in discriminator efficiency and actor diversity to manage noisier or incomplete datasets—a common scenario in real-world applications. Investigating multi-modal policy learning within the DAC framework might also serve to extend its applicability to more complex, high-dimensional action and state spaces often encountered in advanced robotic systems and interactive environments.

Ultimately, the introduction of the DAC algorithm not only provides a robust solution to existing AIL challenges but also establishes a compelling foundation for future exploration and refinement in imitation learning methodologies.