Generative Adversarial Imitation Learning (1606.03476v1)

Abstract: Consider learning a policy from example expert behavior, without interaction with the expert or access to reinforcement signal. One approach is to recover the expert's cost function with inverse reinforcement learning, then extract a policy from that cost function with reinforcement learning. This approach is indirect and can be slow. We propose a new general framework for directly extracting a policy from data, as if it were obtained by reinforcement learning following inverse reinforcement learning. We show that a certain instantiation of our framework draws an analogy between imitation learning and generative adversarial networks, from which we derive a model-free imitation learning algorithm that obtains significant performance gains over existing model-free methods in imitating complex behaviors in large, high-dimensional environments.

Generative Adversarial Imitation Learning

The paper "Generative Adversarial Imitation Learning" by Jonathan Ho and Stefano Ermon presents a novel approach to learning policies from expert demonstrations without interacting with the expert or utilizing reinforcement signals. Traditional imitation learning methods, such as Behavioral Cloning (BC) and Inverse Reinforcement Learning (IRL), respectively face issues of compounding errors due to covariate shift and significant computational expense due to nested RL loops. To address these drawbacks, the paper proposes a new framework aptly named Generative Adversarial Imitation Learning (GAIL).

Introduction

The fundamental goal of imitation learning is to derive a policy that can mimic expert behavior. Traditional methods approach this by either directly learning a policy or inferring a cost function which is later optimized via RL. BC treats the task as a supervised learning problem but suffers from compounding errors, especially with limited data. On the other hand, IRL aims to recover the expert’s cost function which is computationally intensive due to its iterative RL steps. The authors of this paper propose a direct method of learning policies by drawing inspiration from Generative Adversarial Networks (GANs).

Characterization of the Induced Optimal Policy

The research begins by characterizing policies derived from the maximum causal entropy IRL, demonstrating that IRL can be seen as a dual optimization problem where the optimal policy minimizes the distance in occupancy measures between the expert and learner policies. By leveraging this dual relationship, the authors formulate an alternative approach that does not necessitate the intermediate step of learning a cost function but focuses directly on occupancy measure matching, hence sidestepping the computational limitations of conventional IRL.

Generative Adversarial Imitation Learning

The core contribution of the paper is the GAIL framework, which draws a parallel between imitation learning and GANs. In this analogy, the generator corresponds to the policy, and the discriminator evaluates how close the distribution of generated trajectories is to the expert trajectories. Specifically, GAIL optimizes for:

$$\min_{\pi} D_{\text{JS}}(\rho_{\pi} \| \rho_{\pi_E}) - \lambda H(\pi)$$

where $D_{\text{JS}}$ denotes Jensen-Shannon divergence, and $H(\pi)$ represents the causal entropy of the policy. This framework captures the intuition that the policy should generate trajectories that are indistinguishable from expert trajectories according to the discriminator.

Algorithm and Implementation

The practical implementation of GAIL involves parameterizing both the policy and the discriminator with neural networks. The training process alternates between updating the discriminator to better distinguish expert from generated trajectories and updating the policy to generate trajectories that deceive the discriminator. The policy update involves a TRPO step to ensure stable improvements and prevent drastic policy changes driven by noisy gradient estimates.

Experimental Evaluation

Experiments conducted on several control tasks from OpenAI Gym and MuJoCo environments illustrate the superiority of GAIL. These tasks cover a range of complexities, from basic control tasks like Cartpole to high-dimensional, physics-based environments such as Humanoid locomotion. The experimental results indicate that GAIL consistently outperforms BC, Feature Expectation Matching (FEM), and Game-Theoretic Apprenticeship Learning (GTAL) in terms of achieving near-expert performance across these diverse environments.

Practical Implications

The practical implications of this research are significant. GAIL’s model-free nature makes it highly versatile for various high-dimensional tasks. By circumventing the need to learn cost functions explicitly, GAIL reduces the computational burden and simplifies the training process, which is particularly advantageous for real-world applications where collecting extensive expert data or tuning reward functions can be impractical.

Theoretical Implications and Future Directions

From a theoretical perspective, the paper highlights the efficiency and effectiveness of direct policy learning approaches over traditional IRL. The success of GAIL underscores the potential of adversarial methods in reinforcement learning and opens up avenues for further exploration into integrating imitation learning with other advanced RL techniques. Future work could focus on enhancing the sample efficiency of GAIL, potentially by incorporating model-based methods or expert interaction frameworks.

Conclusion

In summary, "Generative Adversarial Imitation Learning" proposes a robust framework that improves upon existing imitation learning methods by directly targeting the learning of policies that generate expert-like trajectories. The paper's contributions have practical and theoretical ramifications that push the boundaries of scalable and effective imitation learning. The methodology and findings pave the way for future research in efficient, model-free imitation learning strategies, promoting further advancements in developing highly capable autonomous systems.