Jaxolotl: A Unified High-Performance Benchmark Suite for LTL-Based Multi-Task RL

Problem

The paper identifies a significant gap in the capability of existing multi-task reinforcement learning (RL) methods, primarily due to the challenges in comparing different approaches. Variations in implementations, task distributions, and evaluation protocols hinder reliable assessments. Additionally, high computational costs restrict the scalability and statistical reliability of experiments. This work is presented as a preprint and has not yet undergone peer review.

Method

The authors propose Jaxolotl, a modular and end-to-end benchmark suite implemented in JAX. This suite includes six representative RL algorithms and four distinct environments, along with newly curated task suites designed for comprehensive evaluation. A standardized and statistically robust evaluation protocol is employed to ensure consistency across experiments. A key optimization technique involves precompiling symbolic task representations into static arrays, which, combined with fully JIT-compiled training and evaluation, results in significant performance improvements. The suite achieves end-to-end speedups of up to 220× compared to traditional implementations, enhancing the efficiency of the benchmarking process.

Results

The benchmark suite demonstrates an impressive speedup of 220×, although no specific baseline for comparison is reported. Evaluation findings indicate that general methods face challenges with non-myopic reasoning as the number of propositions increases. Furthermore, methods that exhibit stronger scaling tend to rely on environment-specific assumptions, which can lead to myopic behavior in decision-making.

Limitations

The authors highlight that general methods struggle with non-myopic reasoning as the complexity of propositions grows. Additionally, the reliance of certain methods on environment-specific assumptions poses a limitation, potentially affecting their generalizability across different tasks and environments. These limitations suggest that while Jaxolotl provides a robust framework for benchmarking, it may not fully address the complexities inherent in multi-task RL scenarios.

Why it matters

The introduction of Jaxolotl has significant implications for the field of multi-task reinforcement learning. By providing a unified and high-performance benchmark suite, it facilitates more reliable comparisons between different methods, potentially accelerating advancements in the area. The standardized evaluation protocol and the ability to achieve substantial speedups can lead to more extensive experimentation and exploration of RL algorithms, ultimately contributing to the development of more effective and scalable solutions in complex environments.