This Python Library ‘Imitation,’ Provides Open-Source Implementations of Imitation and Reward Learning Algorithms in PyTorch

In domains with clearly defined reward functions, such as games, reinforcement learning (RL) has surpassed human performance. Unfortunately, designing a reward function procedurally is difficult or impossible for many real-world tasks. Instead, they must quickly assimilate the reward function or policy from user feedback. Moreover, even though the reward function can be constructed such that the agent wins the game, the resulting objective must be more sparse to effectively solve the RL. Therefore, imitation learning is frequently used to initiate policy in state-of-the-art results for RL.

In this article, they provide simulation, a library that provides an efficient, reliable, and modular implementation of seven reward and simulation learning algorithms. Importantly, the interfaces of their algorithms are consistent, making it easy to train and contrast different methods. Additionally, contemporary backends such as PyTorch and Stable Baselines3 are used to create simulations. Earlier libraries, on the other hand, frequently supported multiple algorithms, were no longer actively updated, and were built on old frameworks. As a baseline for experiments, simulation has many important applications. According to previous research, small implementation details in imitation learning algorithms can significantly affect performance.

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In addition to offering reliable baselines, simulation seeks to simplify the process of creating new reward and simulation learning algorithms. If a poor experimental baseline is used, this may lead to false positive results being reported. Their techniques have been carefully benchmarked and compared to previous solutions to overcome this difficulty. They also perform static type checking and their tests cover 98% of their code. Their implementation is modular, allowing users to flexibly modify the architecture of reward or policy networks, RL algorithms, and optimizers without modifying the code.

By subclassing and overriding required methods, the algorithm can be extended. In addition, simulation offers a practical way to tackle routine activities such as assembling rollouts, which encourages the creation of entirely new algorithms. Another advantage is the fact that the model is built using state-of-the-art frameworks such as PyTorch and Stable Baselines3. In contrast, many current implementations of imitation and reward learning algorithms were published years ago and have not yet been kept up to date. This is especially valid for reference implementations made available with original publications such as the GAIL and AIRL codebases.

Simulation comparison with other algorithms

However, even popular libraries such as Stable Baselines2 are no longer under active development. They compare alternative libraries on various metrics in the table above. Although it is not feasible to include every implementation of imitation and reward learning algorithms, this table covers all widely used imitation learning libraries. They found that simulation equaled or outperformed the alternatives in all metrics. APRL scores high but focuses on learning priority comparison algorithms from low-dimensional features. It is complementary to the model, which provides a wider range of algorithms and emphasizes scalability at the expense of greater implementation complexity. The PyTorch implementation can be found on GitHub.

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Anish Tiku is a Consulting Intern at MarkTechPost. He is currently pursuing a degree in Data Science and Artificial Intelligence from Indian Institute of Technology (IIT), Bhilai. He spends most of his time working on projects aimed at harnessing the power of machine learning. His research interest is image processing and he is passionate about creating solutions around it. He likes connecting with people and collaborating on interesting projects.


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