Learning Robust Rewards with Adversarial Inverse Reinforcement Learning (1710.11248v2)

Abstract: Reinforcement learning provides a powerful and general framework for decision making and control, but its application in practice is often hindered by the need for extensive feature and reward engineering. Deep reinforcement learning methods can remove the need for explicit engineering of policy or value features, but still require a manually specified reward function. Inverse reinforcement learning holds the promise of automatic reward acquisition, but has proven exceptionally difficult to apply to large, high-dimensional problems with unknown dynamics. In this work, we propose adverserial inverse reinforcement learning (AIRL), a practical and scalable inverse reinforcement learning algorithm based on an adversarial reward learning formulation. We demonstrate that AIRL is able to recover reward functions that are robust to changes in dynamics, enabling us to learn policies even under significant variation in the environment seen during training. Our experiments show that AIRL greatly outperforms prior methods in these transfer settings.

Learning Robust Rewards with Adversarial Inverse Reinforcement Learning

The paper "Learning Robust Rewards with Adversarial Inverse Reinforcement Learning" by Justin Fu, Katie Luo, and Sergey Levine addresses significant challenges in reinforcement learning (RL), particularly in the domain of reward engineering. Traditional RL methods necessitate extensive manual specification of reward functions, which can be fraught with difficulties, especially in complex or high-dimensional problem spaces. In contrast, inverse reinforcement learning (IRL) aims to infer reward functions directly from expert demonstrations, promising to mitigate the need for manual reward engineering. Despite this potential, existing IRL methods have struggled to scale to large, high-dimensional environments effectively.

Overview of AIRL

The paper proposes a novel IRL algorithm termed Adversarial Inverse Reinforcement Learning (AIRL), designed to address these issues. AIRL is built on an adversarial reward learning framework, enhancing the ability to learn robust reward functions that generalize across different environments with varying dynamics. The cornerstone of the AIRL algorithm is the discriminator, which concurrently learns the reward function and value function. This allows disentanglement of the reward from the environment dynamics, leading to the recovery of more generalizable and portable reward functions.

Theoretical Contributions

A significant contribution of the paper is the theoretical analysis surrounding the notion of disentangled rewards. The authors provide a formalization showing that in order to ensure robustness against varying dynamics, the learned reward must be a function of state only, not state-action pairs. This disentanglement guarantees that learned rewards remain optimal across different environments. The authors demonstrate that under the maximum causal entropy framework, optimizing the AIRL objective is equivalent to learning a robust reward function.

Experimental Results

Transfer Learning Capacities

The paper evaluates AIRL against prior IRL methods, particularly under domain changes. For instance, in a 2D point mass navigation task with altered maze configurations, AIRL successfully guided the agent to reach the goal in reconfigured mazes. In contrast, baseline methods either underperformed or failed to adapt. The ant locomotion task where the ant's physical configuration was altered between training and test phases further highlighted AIRL’s robustness. Numerical results indicated that AIRL with state-only rewards significantly outperformed alternatives, a testament to its superior generalization capabilities.

Benchmark Imitation Learning Tasks

In traditional imitation learning benchmarks, which do not involve transfer, AIRL exhibited performance on par with the state-of-the-art Generative Adversarial Imitation Learning (GAIL). Across several tasks, including Pendulum, Ant, Swimmer, and Half-Cheetah, AIRL showcased comparable efficiency and effectiveness. This performance parity underscores that while AIRL can generalize better under varied dynamics, it doesn’t compromise on efficiency in standard learning scenarios. This raises an important point: AIRL reconciles the demands of scalability and robustness—attributes often at odds in conventional IRL methods.

Implications and Future Directions

The development of AIRL is a notable step towards more practical and deployable RL systems, where engineering the reward function manually is either infeasible or prone to error. By ensuring that the learned rewards are robust to changes in environmental dynamics, AIRL opens avenues for versatile RL applications, ranging from robotics to autonomous systems operating in dynamic, real-world environments.

The theoretical and empirical insights from this paper also present several avenues for future work. One direction could involve exploring more complex task sets or multi-agent scenarios where interactions dynamically alter the environment. Additionally, future research could investigate scaling AIRL to even more intricate tasks, potentially integrating it with other advancements in model-based RL or hierarchical reinforcement learning to further enhance its applicability and efficiency.

In sum, AIRL represents a significant methodological advancement in the landscape of inverse reinforcement learning, providing a robust framework that both adheres to theoretical rigor and excels in empirical performance.